WorldWideScience

Sample records for automatic speech recognition

  1. Automatic speech recognition a deep learning approach

    CERN Document Server

    Yu, Dong

    2015-01-01

    This book summarizes the recent advancement in the field of automatic speech recognition with a focus on discriminative and hierarchical models. This will be the first automatic speech recognition book to include a comprehensive coverage of recent developments such as conditional random field and deep learning techniques. It presents insights and theoretical foundation of a series of recent models such as conditional random field, semi-Markov and hidden conditional random field, deep neural network, deep belief network, and deep stacking models for sequential learning. It also discusses practical considerations of using these models in both acoustic and language modeling for continuous speech recognition.

  2. Analysis of Phonetic Transcriptions for Danish Automatic Speech Recognition

    DEFF Research Database (Denmark)

    Kirkedal, Andreas Søeborg

    2013-01-01

    Automatic speech recognition (ASR) relies on three resources: audio, orthographic transcriptions and a pronunciation dictionary. The dictionary or lexicon maps orthographic words to sequences of phones or phonemes that represent the pronunciation of the corresponding word. The quality of a speech....... The analysis indicates that transcribing e.g. stress or vowel duration has a negative impact on performance. The best performance is obtained with coarse phonetic annotation and improves performance 1% word error rate and 3.8% sentence error rate....

  3. Mixed Bayesian Networks with Auxiliary Variables for Automatic Speech Recognition

    OpenAIRE

    Stephenson, Todd Andrew; Magimai.-Doss, Mathew; Bourlard, Hervé

    2001-01-01

    Standard hidden Markov models (HMMs), as used in automatic speech recognition (ASR), calculate their emission probabilities by an artificial neural network (ANN) or a Gaussian distribution conditioned on the hidden state variable, considering the emissions independent of any other variable in the model. Recent work showed the benefit of conditioning the emission distributions on a discrete auxiliary variable, which is observed in training and hidden in recognition. Related work has shown the ...

  4. Modelling context in automatic speech recognition

    NARCIS (Netherlands)

    Wiggers, P.

    2008-01-01

    Speech is at the core of human communication. Speaking and listing comes so natural to us that we do not have to think about it at all. The underlying cognitive processes are very rapid and almost completely subconscious. It is hard, if not impossible not to understand speech. For computers on the o

  5. Experiments on Automatic Recognition of Nonnative Arabic Speech

    Directory of Open Access Journals (Sweden)

    Selouani Sid-Ahmed

    2008-01-01

    Full Text Available The automatic recognition of foreign-accented Arabic speech is a challenging task since it involves a large number of nonnative accents. As well, the nonnative speech data available for training are generally insufficient. Moreover, as compared to other languages, the Arabic language has sparked a relatively small number of research efforts. In this paper, we are concerned with the problem of nonnative speech in a speaker independent, large-vocabulary speech recognition system for modern standard Arabic (MSA. We analyze some major differences at the phonetic level in order to determine which phonemes have a significant part in the recognition performance for both native and nonnative speakers. Special attention is given to specific Arabic phonemes. The performance of an HMM-based Arabic speech recognition system is analyzed with respect to speaker gender and its native origin. The WestPoint modern standard Arabic database from the language data consortium (LDC and the hidden Markov Model Toolkit (HTK are used throughout all experiments. Our study shows that the best performance in the overall phoneme recognition is obtained when nonnative speakers are involved in both training and testing phases. This is not the case when a language model and phonetic lattice networks are incorporated in the system. At the phonetic level, the results show that female nonnative speakers perform better than nonnative male speakers, and that emphatic phonemes yield a significant decrease in performance when they are uttered by both male and female nonnative speakers.

  6. Robust Automatic Speech Recognition in Impulsive Noise Environment

    Institute of Scientific and Technical Information of China (English)

    DINGPei; CAOZhigang

    2005-01-01

    This paper presents an efficient method to directly suppress the effect of impulsive noise for robust Automatic speech recognition (ASR). In this method, according to the noise sensitivity of each feature dimension,the observation vectors are divided into several parts, eachof which is assigned to a proper threshold. In recognition stage, the unreliable probability preponderance of incorrect competing path caused by impulsive noise is eliminated by Flooring observation probability (FOP) of eachfeature sub-vector at the Gaussian mixture level, so that the correct path will recover the priority of being chosen in decoding. Experimental results also demonstrate that the proposed method can significantly improve the recognition accuracy both in machinegun noise and simulated impulsive noise environments, while maintaining high performance for clean speech recognition.

  7. Automatic Phonetic Transcription for Danish Speech Recognition

    DEFF Research Database (Denmark)

    Kirkedal, Andreas Søeborg

    to acquire and expensive to create. For languages with productive compounding or agglutinative languages like German and Finnish, respectively, phonetic dictionaries are also hard to maintain. For this reason, automatic phonetic transcription tools have been produced for many languages. The quality...... of automatic phonetic transcriptions vary greatly with respect to language and transcription strategy. For some languages where the difference between the graphemic and phonetic representations are small, graphemic transcriptions can be used to create ASR systems with acceptable performance. In other languages...... representations, e.g. morphological analysis, decompounding, letter-to-sound rules, etc. Two different phonetic transcribers for Danish will be compared in this study: eSpeak (Duddington, 2010) and Phonix (Henrichsen, 2014). Both transcribers produce a richer transcription than ASR can utilise such as stress...

  8. The influence of age, hearing, and working memory on the speech comprehension benefit derived from an automatic speech recognition system

    NARCIS (Netherlands)

    Zekveld, A.A.; Kramer, S.E.; Kessens, J.M.; Vlaming, M.S.M.G.; Houtgast, T.

    2009-01-01

    Objective: The aim of the current study was to examine whether partly incorrect subtitles that are automatically generated by an Automatic Speech Recognition (ASR) system, improve speech comprehension by listeners with hearing impairment. In an earlier study (Zekveld et al. 2008), we showed that spe

  9. Automatic Speech Recognition Systems for the Evaluation of Voice and Speech Disorders in Head and Neck Cancer

    Directory of Open Access Journals (Sweden)

    Andreas Maier

    2010-01-01

    Full Text Available In patients suffering from head and neck cancer, speech intelligibility is often restricted. For assessment and outcome measurements, automatic speech recognition systems have previously been shown to be appropriate for objective and quick evaluation of intelligibility. In this study we investigate the applicability of the method to speech disorders caused by head and neck cancer. Intelligibility was quantified by speech recognition on recordings of a standard text read by 41 German laryngectomized patients with cancer of the larynx or hypopharynx and 49 German patients who had suffered from oral cancer. The speech recognition provides the percentage of correctly recognized words of a sequence, that is, the word recognition rate. Automatic evaluation was compared to perceptual ratings by a panel of experts and to an age-matched control group. Both patient groups showed significantly lower word recognition rates than the control group. Automatic speech recognition yielded word recognition rates which complied with experts' evaluation of intelligibility on a significant level. Automatic speech recognition serves as a good means with low effort to objectify and quantify the most important aspect of pathologic speech—the intelligibility. The system was successfully applied to voice and speech disorders.

  10. Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

    Science.gov (United States)

    Huang, Yiteng; Chen, Jingdong; Chen, Shaoyan

    2010-01-01

    A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

  11. Studies on inter-speaker variability in speech and its application in automatic speech recognition

    Indian Academy of Sciences (India)

    S Umesh

    2011-10-01

    In this paper, we give an overview of the problem of inter-speaker variability and its study in many diverse areas of speech signal processing. We first give an overview of vowel-normalization studies that minimize variations in the acoustic representation of vowel realizations by different speakers. We then describe the universal-warping approach to speaker normalization which unifies many of the vowel normalization approaches and also shows the relation between speech production, perception and auditory processing. We then address the problem of inter-speaker variability in automatic speech recognition (ASR) and describe techniques that are used to reduce these effects and thereby improve the performance of speaker-independent ASR systems.

  12. Studies in automatic speech recognition and its application in aerospace

    Science.gov (United States)

    Taylor, Michael Robinson

    Human communication is characterized in terms of the spectral and temporal dimensions of speech waveforms. Electronic speech recognition strategies based on Dynamic Time Warping and Markov Model algorithms are described and typical digit recognition error rates are tabulated. The application of Direct Voice Input (DVI) as an interface between man and machine is explored within the context of civil and military aerospace programmes. Sources of physical and emotional stress affecting speech production within military high performance aircraft are identified. Experimental results are reported which quantify fundamental frequency and coarse temporal dimensions of male speech as a function of the vibration, linear acceleration and noise levels typical of aerospace environments; preliminary indications of acoustic phonetic variability reported by other researchers are summarized. Connected whole-word pattern recognition error rates are presented for digits spoken under controlled Gz sinusoidal whole-body vibration. Correlations are made between significant increases in recognition error rate and resonance of the abdomen-thorax and head subsystems of the body. The phenomenon of vibrato style speech produced under low frequency whole-body Gz vibration is also examined. Interactive DVI system architectures and avionic data bus integration concepts are outlined together with design procedures for the efficient development of pilot-vehicle command and control protocols.

  13. Developing and Evaluating an Oral Skills Training Website Supported by Automatic Speech Recognition Technology

    Science.gov (United States)

    Chen, Howard Hao-Jan

    2011-01-01

    Oral communication ability has become increasingly important to many EFL students. Several commercial software programs based on automatic speech recognition (ASR) technologies are available but their prices are not affordable for many students. This paper will demonstrate how the Microsoft Speech Application Software Development Kit (SASDK), a…

  14. An open-set detection evaluation methodology for automatic emotion recognition in speech

    NARCIS (Netherlands)

    Truong, K.P.; Leeuwen, D.A. van

    2007-01-01

    In this paper, we present a detection approach and an ‘open-set’ detection evaluation methodology for automatic emotion recognition in speech. The traditional classification approach does not seem to be suitable and flexible enough for typical emotion recognition tasks. For example, classification d

  15. Fusing Eye-gaze and Speech Recognition for Tracking in an Automatic Reading Tutor

    DEFF Research Database (Denmark)

    Rasmussen, Morten Højfeldt; Tan, Zheng-Hua

    2013-01-01

    In this paper we present a novel approach for automatically tracking the reading progress using a combination of eye-gaze tracking and speech recognition. The two are fused by first generating word probabilities based on eye-gaze information and then using these probabilities to augment the langu......In this paper we present a novel approach for automatically tracking the reading progress using a combination of eye-gaze tracking and speech recognition. The two are fused by first generating word probabilities based on eye-gaze information and then using these probabilities to augment...

  16. Evaluating Automatic Speech Recognition-Based Language Learning Systems: A Case Study

    Science.gov (United States)

    van Doremalen, Joost; Boves, Lou; Colpaert, Jozef; Cucchiarini, Catia; Strik, Helmer

    2016-01-01

    The purpose of this research was to evaluate a prototype of an automatic speech recognition (ASR)-based language learning system that provides feedback on different aspects of speaking performance (pronunciation, morphology and syntax) to students of Dutch as a second language. We carried out usability reviews, expert reviews and user tests to…

  17. Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech.

    Science.gov (United States)

    Agarwalla, Swapna; Sarma, Kandarpa Kumar

    2016-06-01

    Automatic Speaker Recognition (ASR) and related issues are continuously evolving as inseparable elements of Human Computer Interaction (HCI). With assimilation of emerging concepts like big data and Internet of Things (IoT) as extended elements of HCI, ASR techniques are found to be passing through a paradigm shift. Oflate, learning based techniques have started to receive greater attention from research communities related to ASR owing to the fact that former possess natural ability to mimic biological behavior and that way aids ASR modeling and processing. The current learning based ASR techniques are found to be evolving further with incorporation of big data, IoT like concepts. Here, in this paper, we report certain approaches based on machine learning (ML) used for extraction of relevant samples from big data space and apply them for ASR using certain soft computing techniques for Assamese speech with dialectal variations. A class of ML techniques comprising of the basic Artificial Neural Network (ANN) in feedforward (FF) and Deep Neural Network (DNN) forms using raw speech, extracted features and frequency domain forms are considered. The Multi Layer Perceptron (MLP) is configured with inputs in several forms to learn class information obtained using clustering and manual labeling. DNNs are also used to extract specific sentence types. Initially, from a large storage, relevant samples are selected and assimilated. Next, a few conventional methods are used for feature extraction of a few selected types. The features comprise of both spectral and prosodic types. These are applied to Recurrent Neural Network (RNN) and Fully Focused Time Delay Neural Network (FFTDNN) structures to evaluate their performance in recognizing mood, dialect, speaker and gender variations in dialectal Assamese speech. The system is tested under several background noise conditions by considering the recognition rates (obtained using confusion matrices and manually) and computation time

  18. Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech.

    Science.gov (United States)

    Agarwalla, Swapna; Sarma, Kandarpa Kumar

    2016-06-01

    Automatic Speaker Recognition (ASR) and related issues are continuously evolving as inseparable elements of Human Computer Interaction (HCI). With assimilation of emerging concepts like big data and Internet of Things (IoT) as extended elements of HCI, ASR techniques are found to be passing through a paradigm shift. Oflate, learning based techniques have started to receive greater attention from research communities related to ASR owing to the fact that former possess natural ability to mimic biological behavior and that way aids ASR modeling and processing. The current learning based ASR techniques are found to be evolving further with incorporation of big data, IoT like concepts. Here, in this paper, we report certain approaches based on machine learning (ML) used for extraction of relevant samples from big data space and apply them for ASR using certain soft computing techniques for Assamese speech with dialectal variations. A class of ML techniques comprising of the basic Artificial Neural Network (ANN) in feedforward (FF) and Deep Neural Network (DNN) forms using raw speech, extracted features and frequency domain forms are considered. The Multi Layer Perceptron (MLP) is configured with inputs in several forms to learn class information obtained using clustering and manual labeling. DNNs are also used to extract specific sentence types. Initially, from a large storage, relevant samples are selected and assimilated. Next, a few conventional methods are used for feature extraction of a few selected types. The features comprise of both spectral and prosodic types. These are applied to Recurrent Neural Network (RNN) and Fully Focused Time Delay Neural Network (FFTDNN) structures to evaluate their performance in recognizing mood, dialect, speaker and gender variations in dialectal Assamese speech. The system is tested under several background noise conditions by considering the recognition rates (obtained using confusion matrices and manually) and computation time

  19. Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

    Science.gov (United States)

    Fernández Pozo, Rubén; Blanco Murillo, Jose Luis; Hernández Gómez, Luis; López Gonzalo, Eduardo; Alcázar Ramírez, José; Toledano, Doroteo T.

    2009-12-01

    This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

  20. Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

    Directory of Open Access Journals (Sweden)

    Rubén Fernández Pozo

    2009-01-01

    Full Text Available This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA. Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

  1. Physiologically Motivated Feature Extraction for Robust Automatic Speech Recognition

    OpenAIRE

    Ibrahim Missaoui; Zied Lachiri

    2016-01-01

    In this paper, a new method is presented to extract robust speech features in the presence of the external noise. The proposed method based on two-dimensional Gabor filters takes in account the spectro-temporal modulation frequencies and also limits the redundancy on the feature level. The performance of the proposed feature extraction method was evaluated on isolated speech words which are extracted from TIMIT corpus and corrupted by background noise. The evaluation results demonstrate that ...

  2. An HMM-Like Dynamic Time Warping Scheme for Automatic Speech Recognition

    Directory of Open Access Journals (Sweden)

    Ing-Jr Ding

    2014-01-01

    Full Text Available In the past, the kernel of automatic speech recognition (ASR is dynamic time warping (DTW, which is feature-based template matching and belongs to the category technique of dynamic programming (DP. Although DTW is an early developed ASR technique, DTW has been popular in lots of applications. DTW is playing an important role for the known Kinect-based gesture recognition application now. This paper proposed an intelligent speech recognition system using an improved DTW approach for multimedia and home automation services. The improved DTW presented in this work, called HMM-like DTW, is essentially a hidden Markov model- (HMM- like method where the concept of the typical HMM statistical model is brought into the design of DTW. The developed HMM-like DTW method, transforming feature-based DTW recognition into model-based DTW recognition, will be able to behave as the HMM recognition technique and therefore proposed HMM-like DTW with the HMM-like recognition model will have the capability to further perform model adaptation (also known as speaker adaptation. A series of experimental results in home automation-based multimedia access service environments demonstrated the superiority and effectiveness of the developed smart speech recognition system by HMM-like DTW.

  3. Physiologically Motivated Feature Extraction for Robust Automatic Speech Recognition

    Directory of Open Access Journals (Sweden)

    Ibrahim Missaoui

    2016-04-01

    Full Text Available In this paper, a new method is presented to extract robust speech features in the presence of the external noise. The proposed method based on two-dimensional Gabor filters takes in account the spectro-temporal modulation frequencies and also limits the redundancy on the feature level. The performance of the proposed feature extraction method was evaluated on isolated speech words which are extracted from TIMIT corpus and corrupted by background noise. The evaluation results demonstrate that the proposed feature extraction method outperforms the classic methods such as Perceptual Linear Prediction, Linear Predictive Coding, Linear Prediction Cepstral coefficients and Mel Frequency Cepstral Coefficients.

  4. An FFT-Based Companding Front End for Noise-Robust Automatic Speech Recognition

    Directory of Open Access Journals (Sweden)

    Turicchia Lorenzo

    2007-01-01

    Full Text Available We describe an FFT-based companding algorithm for preprocessing speech before recognition. The algorithm mimics tone-to-tone suppression and masking in the auditory system to improve automatic speech recognition performance in noise. Moreover, it is also very computationally efficient and suited to digital implementations due to its use of the FFT. In an automotive digits recognition task with the CU-Move database recorded in real environmental noise, the algorithm improves the relative word error by 12.5% at -5 dB signal-to-noise ratio (SNR and by 6.2% across all SNRs (-5 dB SNR to +5 dB SNR. In the Aurora-2 database recorded with artificially added noise in several environments, the algorithm improves the relative word error rate in almost all situations.

  5. An FFT-Based Companding Front End for Noise-Robust Automatic Speech Recognition

    Directory of Open Access Journals (Sweden)

    Bhiksha Raj

    2007-06-01

    Full Text Available We describe an FFT-based companding algorithm for preprocessing speech before recognition. The algorithm mimics tone-to-tone suppression and masking in the auditory system to improve automatic speech recognition performance in noise. Moreover, it is also very computationally efficient and suited to digital implementations due to its use of the FFT. In an automotive digits recognition task with the CU-Move database recorded in real environmental noise, the algorithm improves the relative word error by 12.5% at −5 dB signal-to-noise ratio (SNR and by 6.2% across all SNRs (−5 dB SNR to +15 dB SNR. In the Aurora-2 database recorded with artificially added noise in several environments, the algorithm improves the relative word error rate in almost all situations.

  6. A HYBRID METHOD FOR AUTOMATIC SPEECH RECOGNITION PERFORMANCE IMPROVEMENT IN REAL WORLD NOISY ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    Urmila Shrawankar

    2013-01-01

    Full Text Available It is a well known fact that, speech recognition systems perform well when the system is used in conditions similar to the one used to train the acoustic models. However, mismatches degrade the performance. In adverse environment, it is very difficult to predict the category of noise in advance in case of real world environmental noise and difficult to achieve environmental robustness. After doing rigorous experimental study it is observed that, a unique method is not available that will clean the noisy speech as well as preserve the quality which have been corrupted by real natural environmental (mixed noise. It is also observed that only back-end techniques are not sufficient to improve the performance of a speech recognition system. It is necessary to implement performance improvement techniques at every step of back-end as well as front-end of the Automatic Speech Recognition (ASR model. Current recognition systems solve this problem using a technique called adaptation. This study presents an experimental study that aims two points, first is to implement the hybrid method that will take care of clarifying the speech signal as much as possible with all combinations of filters and enhancement techniques. The second point is to develop a method for training all categories of noise that can adapt the acoustic models for a new environment that will help to improve the performance of the speech recognizer under real world environmental mismatched conditions. This experiment confirms that hybrid adaptation methods improve the ASR performance on both levels, (Signal-to-Noise Ratio SNR improvement as well as word recognition accuracy in real world noisy environment.

  7. Contribution to automatic speech recognition. Analysis of the direct acoustical signal. Recognition of isolated words and phoneme identification

    International Nuclear Information System (INIS)

    This report deals with the acoustical-phonetic step of the automatic recognition of the speech. The parameters used are the extrema of the acoustical signal (coded in amplitude and duration). This coding method, the properties of which are described, is simple and well adapted to a digital processing. The quality and the intelligibility of the coded signal after reconstruction are particularly satisfactory. An experiment for the automatic recognition of isolated words has been carried using this coding system. We have designed a filtering algorithm operating on the parameters of the coding. Thus the characteristics of the formants can be derived under certain conditions which are discussed. Using these characteristics the identification of a large part of the phonemes for a given speaker was achieved. Carrying on the studies has required the development of a particular methodology of real time processing which allowed immediate evaluation of the improvement of the programs. Such processing on temporal coding of the acoustical signal is extremely powerful and could represent, used in connection with other methods an efficient tool for the automatic processing of the speech.(author)

  8. Towards an Intelligent Acoustic Front End for Automatic Speech Recognition: Built-in Speaker Normalization

    Directory of Open Access Journals (Sweden)

    Umit H. Yapanel

    2008-08-01

    Full Text Available A proven method for achieving effective automatic speech recognition (ASR due to speaker differences is to perform acoustic feature speaker normalization. More effective speaker normalization methods are needed which require limited computing resources for real-time performance. The most popular speaker normalization technique is vocal-tract length normalization (VTLN, despite the fact that it is computationally expensive. In this study, we propose a novel online VTLN algorithm entitled built-in speaker normalization (BISN, where normalization is performed on-the-fly within a newly proposed PMVDR acoustic front end. The novel algorithm aspect is that in conventional frontend processing with PMVDR and VTLN, two separating warping phases are needed; while in the proposed BISN method only one single speaker dependent warp is used to achieve both the PMVDR perceptual warp and VTLN warp simultaneously. This improved integration unifies the nonlinear warping performed in the front end and reduces simultaneously. This improved integration unifies the nonlinear warping performed in the front end and reduces computational requirements, thereby offering advantages for real-time ASR systems. Evaluations are performed for (i an in-car extended digit recognition task, where an on-the-fly BISN implementation reduces the relative word error rate (WER by 24%, and (ii for a diverse noisy speech task (SPINE 2, where the relative WER improvement was 9%, both relative to the baseline speaker normalization method.

  9. Towards an Intelligent Acoustic Front End for Automatic Speech Recognition: Built-in Speaker Normalization

    Directory of Open Access Journals (Sweden)

    Yapanel UmitH

    2008-01-01

    Full Text Available A proven method for achieving effective automatic speech recognition (ASR due to speaker differences is to perform acoustic feature speaker normalization. More effective speaker normalization methods are needed which require limited computing resources for real-time performance. The most popular speaker normalization technique is vocal-tract length normalization (VTLN, despite the fact that it is computationally expensive. In this study, we propose a novel online VTLN algorithm entitled built-in speaker normalization (BISN, where normalization is performed on-the-fly within a newly proposed PMVDR acoustic front end. The novel algorithm aspect is that in conventional frontend processing with PMVDR and VTLN, two separating warping phases are needed; while in the proposed BISN method only one single speaker dependent warp is used to achieve both the PMVDR perceptual warp and VTLN warp simultaneously. This improved integration unifies the nonlinear warping performed in the front end and reduces simultaneously. This improved integration unifies the nonlinear warping performed in the front end and reduces computational requirements, thereby offering advantages for real-time ASR systems. Evaluations are performed for (i an in-car extended digit recognition task, where an on-the-fly BISN implementation reduces the relative word error rate (WER by 24%, and (ii for a diverse noisy speech task (SPINE 2, where the relative WER improvement was 9%, both relative to the baseline speaker normalization method.

  10. An exploration of the potential of Automatic Speech Recognition to assist and enable receptive communication in higher education

    Directory of Open Access Journals (Sweden)

    Mike Wald

    2006-12-01

    Full Text Available The potential use of Automatic Speech Recognition to assist receptive communication is explored. The opportunities and challenges that this technology presents students and staff to provide captioning of speech online or in classrooms for deaf or hard of hearing students and assist blind, visually impaired or dyslexic learners to read and search learning material more readily by augmenting synthetic speech with natural recorded real speech is also discussed and evaluated. The automatic provision of online lecture notes, synchronised with speech, enables staff and students to focus on learning and teaching issues, while also benefiting learners unable to attend the lecture or who find it difficult or impossible to take notes at the same time as listening, watching and thinking.

  11. Automatic Speech Recognition Using Template Model for Man-Machine Interface

    OpenAIRE

    Mishra, Neema; Shrawankar, Urmila; Thakare, V. M

    2013-01-01

    Speech is a natural form of communication for human beings, and computers with the ability to understand speech and speak with a human voice are expected to contribute to the development of more natural man-machine interfaces. Computers with this kind of ability are gradually becoming a reality, through the evolution of speech recognition technologies. Speech is being an important mode of interaction with computers. In this paper Feature extraction is implemented using well-known Mel-Frequenc...

  12. Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish.

    Science.gov (United States)

    Vieira, Manuel; Fonseca, Paulo J; Amorim, M Clara P; Teixeira, Carlos J C

    2015-12-01

    The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types. PMID:26723348

  13. Exploiting independent filter bandwidth of human factor cepstral coefficients in automatic speech recognition

    Science.gov (United States)

    Skowronski, Mark D.; Harris, John G.

    2004-09-01

    Mel frequency cepstral coefficients (MFCC) are the most widely used speech features in automatic speech recognition systems, primarily because the coefficients fit well with the assumptions used in hidden Markov models and because of the superior noise robustness of MFCC over alternative feature sets such as linear prediction-based coefficients. The authors have recently introduced human factor cepstral coefficients (HFCC), a modification of MFCC that uses the known relationship between center frequency and critical bandwidth from human psychoacoustics to decouple filter bandwidth from filter spacing. In this work, the authors introduce a variation of HFCC called HFCC-E in which filter bandwidth is linearly scaled in order to investigate the effects of wider filter bandwidth on noise robustness. Experimental results show an increase in signal-to-noise ratio of 7 dB over traditional MFCC algorithms when filter bandwidth increases in HFCC-E. An important attribute of both HFCC and HFCC-E is that the algorithms only differ from MFCC in the filter bank coefficients: increased noise robustness using wider filters is achieved with no additional computational cost.

  14. Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients

    Directory of Open Access Journals (Sweden)

    TjongWan Sen

    2009-11-01

    Full Text Available To improve the performance of phoneme based Automatic Speech Recognition (ASR in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA. These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4 from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments.

  15. Pattern recognition in speech and language processing

    CERN Document Server

    Chou, Wu

    2003-01-01

    Minimum Classification Error (MSE) Approach in Pattern Recognition, Wu ChouMinimum Bayes-Risk Methods in Automatic Speech Recognition, Vaibhava Goel and William ByrneA Decision Theoretic Formulation for Adaptive and Robust Automatic Speech Recognition, Qiang HuoSpeech Pattern Recognition Using Neural Networks, Shigeru KatagiriLarge Vocabulary Speech Recognition Based on Statistical Methods, Jean-Luc GauvainToward Spontaneous Speech Recognition and Understanding, Sadaoki FuruiSpeaker Authentication, Qi Li and Biing-Hwang JuangHMMs for Language Processing Problems, Ri

  16. User Experience of a Mobile Speaking Application with Automatic Speech Recognition for EFL Learning

    Science.gov (United States)

    Ahn, Tae youn; Lee, Sangmin-Michelle

    2016-01-01

    With the spread of mobile devices, mobile phones have enormous potential regarding their pedagogical use in language education. The goal of this study is to analyse user experience of a mobile-based learning system that is enhanced by speech recognition technology for the improvement of EFL (English as a foreign language) learners' speaking…

  17. Dynamic time warping applied to detection of confusable word pairs in automatic speech recognition

    OpenAIRE

    Anguita Ortega, Jan; Hernando Pericás, Francisco Javier

    2005-01-01

    In this paper we present a rnethod to predict if two words are likely to be confused by an Autornatic SpeechRecognition (ASR) systern. This method is based on the c1assical Dynamic Time Warping (DTW) technique. This technique, which is usually used in ASR to measure the distance between two speech signals, is usedhere to calculate the distance between two words. With this distance the words are c1assified as confusable or not confusable using a threshold. We have te...

  18. Estimation of Phoneme-Specific HMM Topologies for the Automatic Recognition of Dysarthric Speech

    Directory of Open Access Journals (Sweden)

    Santiago-Omar Caballero-Morales

    2013-01-01

    Full Text Available Dysarthria is a frequently occurring motor speech disorder which can be caused by neurological trauma, cerebral palsy, or degenerative neurological diseases. Because dysarthria affects phonation, articulation, and prosody, spoken communication of dysarthric speakers gets seriously restricted, affecting their quality of life and confidence. Assistive technology has led to the development of speech applications to improve the spoken communication of dysarthric speakers. In this field, this paper presents an approach to improve the accuracy of HMM-based speech recognition systems. Because phonatory dysfunction is a main characteristic of dysarthric speech, the phonemes of a dysarthric speaker are affected at different levels. Thus, the approach consists in finding the most suitable type of HMM topology (Bakis, Ergodic for each phoneme in the speaker’s phonetic repertoire. The topology is further refined with a suitable number of states and Gaussian mixture components for acoustic modelling. This represents a difference when compared with studies where a single topology is assumed for all phonemes. Finding the suitable parameters (topology and mixtures components is performed with a Genetic Algorithm (GA. Experiments with a well-known dysarthric speech database showed statistically significant improvements of the proposed approach when compared with the single topology approach, even for speakers with severe dysarthria.

  19. An automatic speech recognition system with speaker-independent identification support

    Science.gov (United States)

    Caranica, Alexandru; Burileanu, Corneliu

    2015-02-01

    The novelty of this work relies on the application of an open source research software toolkit (CMU Sphinx) to train, build and evaluate a speech recognition system, with speaker-independent support, for voice-controlled hardware applications. Moreover, we propose to use the trained acoustic model to successfully decode offline voice commands on embedded hardware, such as an ARMv6 low-cost SoC, Raspberry PI. This type of single-board computer, mainly used for educational and research activities, can serve as a proof-of-concept software and hardware stack for low cost voice automation systems.

  20. Speech Recognition on Mobile Devices

    DEFF Research Database (Denmark)

    Tan, Zheng-Hua; Lindberg, Børge

    2010-01-01

    The enthusiasm of deploying automatic speech recognition (ASR) on mobile devices is driven both by remarkable advances in ASR technology and by the demand for efficient user interfaces on such devices as mobile phones and personal digital assistants (PDAs). This chapter presents an overview of ASR...

  1. Novel Techniques for Dialectal Arabic Speech Recognition

    CERN Document Server

    Elmahdy, Mohamed; Minker, Wolfgang

    2012-01-01

    Novel Techniques for Dialectal Arabic Speech describes approaches to improve automatic speech recognition for dialectal Arabic. Since speech resources for dialectal Arabic speech recognition are very sparse, the authors describe how existing Modern Standard Arabic (MSA) speech data can be applied to dialectal Arabic speech recognition, while assuming that MSA is always a second language for all Arabic speakers. In this book, Egyptian Colloquial Arabic (ECA) has been chosen as a typical Arabic dialect. ECA is the first ranked Arabic dialect in terms of number of speakers, and a high quality ECA speech corpus with accurate phonetic transcription has been collected. MSA acoustic models were trained using news broadcast speech. In order to cross-lingually use MSA in dialectal Arabic speech recognition, the authors have normalized the phoneme sets for MSA and ECA. After this normalization, they have applied state-of-the-art acoustic model adaptation techniques like Maximum Likelihood Linear Regression (MLLR) and M...

  2. Automatic Speech Recognition and Training for Severely Dysarthric Users of Assistive Technology: The STARDUST Project

    Science.gov (United States)

    Parker, Mark; Cunningham, Stuart; Enderby, Pam; Hawley, Mark; Green, Phil

    2006-01-01

    The STARDUST project developed robust computer speech recognizers for use by eight people with severe dysarthria and concomitant physical disability to access assistive technologies. Independent computer speech recognizers trained with normal speech are of limited functional use by those with severe dysarthria due to limited and inconsistent…

  3. Speech recognition from spectral dynamics

    Indian Academy of Sciences (India)

    Hynek Hermansky

    2011-10-01

    Information is carried in changes of a signal. The paper starts with revisiting Dudley’s concept of the carrier nature of speech. It points to its close connection to modulation spectra of speech and argues against short-term spectral envelopes as dominant carriers of the linguistic information in speech. The history of spectral representations of speech is briefly discussed. Some of the history of gradual infusion of the modulation spectrum concept into Automatic recognition of speech (ASR) comes next, pointing to the relationship of modulation spectrum processing to wellaccepted ASR techniques such as dynamic speech features or RelAtive SpecTrAl (RASTA) filtering. Next, the frequency domain perceptual linear prediction technique for deriving autoregressive models of temporal trajectories of spectral power in individual frequency bands is reviewed. Finally, posterior-based features, which allow for straightforward application of modulation frequency domain information, are described. The paper is tutorial in nature, aims at a historical global overview of attempts for using spectral dynamics in machine recognition of speech, and does not always provide enough detail of the described techniques. However, extensive references to earlier work are provided to compensate for the lack of detail in the paper.

  4. Automatic recognition of spontaneous emotions in speech using acoustic and lexical features

    NARCIS (Netherlands)

    Raaijmakers, S.; Truong, K.P.

    2008-01-01

    We developed acoustic and lexical classifiers, based on a boosting algorithm, to assess the separability on arousal and valence dimensions in spontaneous emotional speech. The spontaneous emotional speech data was acquired by inviting subjects to play a first-person shooter video game. Our acoustic

  5. Advances in Speech Recognition

    CERN Document Server

    Neustein, Amy

    2010-01-01

    This volume is comprised of contributions from eminent leaders in the speech industry, and presents a comprehensive and in depth analysis of the progress of speech technology in the topical areas of mobile settings, healthcare and call centers. The material addresses the technical aspects of voice technology within the framework of societal needs, such as the use of speech recognition software to produce up-to-date electronic health records, not withstanding patients making changes to health plans and physicians. Included will be discussion of speech engineering, linguistics, human factors ana

  6. Separable spectro-temporal Gabor filter bank features: Reducing the complexity of robust features for automatic speech recognition.

    Science.gov (United States)

    Schädler, Marc René; Kollmeier, Birger

    2015-04-01

    To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [J. Acoust. Soc. Am. 131, 4134-4151 (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor filter bank. A feature set that is extracted with these separate spectral and temporal modulation filter banks was introduced, the separate Gabor filter bank (SGBFB) features, and evaluated on the CHiME (Computational Hearing in Multisource Environments) keywords-in-noise recognition task. From the perspective of robust ASR, the results showed that spectral and temporal processing can be performed independently and are not required to interact with each other. Using SGBFB features permitted the signal-to-noise ratio (SNR) to be lowered by 1.2 dB while still performing as well as the GBFB-based reference system, which corresponds to a relative improvement of the word error rate by 12.8%. Additionally, the real time factor of the spectro-temporal processing could be reduced by more than an order of magnitude. Compared to human listeners, the SNR needed to be 13 dB higher when using Mel-frequency cepstral coefficient features, 11 dB higher when using GBFB features, and 9 dB higher when using SGBFB features to achieve the same recognition performance. PMID:25920855

  7. Noise robust automatic speech recognition with adaptive quantile based noise estimation and speech band emphasizing filter bank

    DEFF Research Database (Denmark)

    Bonde, Casper Stork; Graversen, Carina; Gregersen, Andreas Gregers;

    2005-01-01

    to the appearance of the speech signal which require noise robust voice activity detection and assumptions of stationary noise. However, both of these requirements are often not met and it is therefore of particular interest to investigate methods like the Quantile Based Noise Estimation (QBNE) mehtod which...

  8. Robust speech recognition using articulatory information

    OpenAIRE

    Kirchhoff, Katrin

    1999-01-01

    Current automatic speech recognition systems make use of a single source of information about their input, viz. a preprocessed form of the acoustic speech signal, which encodes the time-frequency distribution of signal energy. The goal of this thesis is to investigate the benefits of integrating articulatory information into state-of-the art speech recognizers, either as a genuine alternative to standard acoustic representations, or as an additional source of information. Articulatory informa...

  9. Recognition of Isolated Words using Zernike and MFCC features for Audio Visual Speech Recognition

    OpenAIRE

    Bordea, Prashant; Varpeb, Amarsinh; Manzac, Ramesh; Yannawara, Pravin

    2014-01-01

    Automatic Speech Recognition (ASR) by machine is an attractive research topic in signal processing domain and has attracted many researchers to contribute in this area. In recent year, there have been many advances in automatic speech reading system with the inclusion of audio and visual speech features to recognize words under noisy conditions. The objective of audio-visual speech recognition system is to improve recognition accuracy. In this paper we computed visual features using Zernike m...

  10. Integrating Automatic Speech Recognition and Machine Translation for Better Translation Outputs

    DEFF Research Database (Denmark)

    Liyanapathirana, Jeevanthi

    than typing, making the translation process faster. The spoken translation is analyzed and combined with the machine translation output of the same sentence using different methods. We study a number of different translation models in the context of n-best list rescoring methods. As an alternative...... to the n-best list rescoring, we also use word graphs with the expectation of arriving at a tighter integration of ASR and MT models. Integration methods include constraining ASR models using language and translation models of MT, and vice versa. We currently develop and experiment different methods...... on the Danish – English language pair, with the use of a speech corpora and parallel text. The methods are investigated to check ways that the accuracy of the spoken translation of the translator can be increased with the use of machine translation outputs, which would be useful for potential computer...

  11. Optimizing Automatic Speech Recognition for Low-Proficient Non-Native Speakers

    Directory of Open Access Journals (Sweden)

    Catia Cucchiarini

    2010-01-01

    Full Text Available Computer-Assisted Language Learning (CALL applications for improving the oral skills of low-proficient learners have to cope with non-native speech that is particularly challenging. Since unconstrained non-native ASR is still problematic, a possible solution is to elicit constrained responses from the learners. In this paper, we describe experiments aimed at selecting utterances from lists of responses. The first experiment on utterance selection indicates that the decoding process can be improved by optimizing the language model and the acoustic models, thus reducing the utterance error rate from 29–26% to 10–8%. Since giving feedback on incorrectly recognized utterances is confusing, we verify the correctness of the utterance before providing feedback. The results of the second experiment on utterance verification indicate that combining duration-related features with a likelihood ratio (LR yield an equal error rate (EER of 10.3%, which is significantly better than the EER for the other measures in isolation.

  12. Speech recognition in university classrooms

    OpenAIRE

    Wald, Mike; Bain, Keith; Basson, Sara H

    2002-01-01

    The LIBERATED LEARNING PROJECT (LLP) is an applied research project studying two core questions: 1) Can speech recognition (SR) technology successfully digitize lectures to display spoken words as text in university classrooms? 2) Can speech recognition technology be used successfully as an alternative to traditional classroom notetaking for persons with disabilities? This paper addresses these intriguing questions and explores the underlying complex relationship between speech recognition te...

  13. Phonetic Alphabet for Speech Recognition of Czech

    OpenAIRE

    J. Uhlir; Psutka, J.; J. Nouza

    1997-01-01

    In the paper we introduce and discuss an alphabet that has been proposed for phonemicly oriented automatic speech recognition. The alphabet, denoted as a PAC (Phonetic Alphabet for Czech) consists of 48 basic symbols that allow for distinguishing all major events occurring in spoken Czech language. The symbols can be used both for phonetic transcription of Czech texts as well as for labeling recorded speech signals. From practical reasons, the alphabet occurs in two versions; one utilizes Cze...

  14. Speech recognition employing biologically plausible receptive fields

    DEFF Research Database (Denmark)

    Fereczkowski, Michal; Bothe, Hans-Heinrich

    2011-01-01

    The main idea of the project is to build a widely speaker-independent, biologically motivated automatic speech recognition (ASR) system. The two main differences between our approach and current state-of-the-art ASRs are that i) the features used here are based on the responses of neuronlike spec...

  15. The Use of Speech Recognition Technology in Automotive Applications

    OpenAIRE

    Gellatly, Andrew William

    1997-01-01

    The research objectives were (1) to perform a detailed review of the literature on speech recognition technology and the attentional demands of driving; (2) to develop decision tools that assist designers of in-vehicle systems; (3) to experimentally examine automatic speech recognition (ASR) design parameters, input modalities, and driver ages; and (4) to provide human factors recommendations for the use of speech recognition technology in automotive applicatio...

  16. Robust Speech Recognition Using a Harmonic Model

    Institute of Scientific and Technical Information of China (English)

    许超; 曹志刚

    2004-01-01

    Automatic speech recognition under conditions of a noisy environment remains a challenging problem. Traditionally, methods focused on noise structure, such as spectral subtraction, have been employed to address this problem, and thus the performance of such methods depends on the accuracy in noise estimation. In this paper, an alternative method, using a harmonic-based spectral reconstruction algorithm, is proposed for the enhancement of robust automatic speech recognition. Neither noise estimation nor noise-model training are required in the proposed approach. A spectral subtraction integrated autocorrelation function is proposed to determine the pitch for the harmonic model. Recognition results show that the harmonic-based spectral reconstruction approach outperforms spectral subtraction in the middle- and low-signal noise ratio (SNR) ranges. The advantage of the proposed method is more manifest for non-stationary noise, as the algorithm does not require an assumption of stationary noise.

  17. Multi-thread Parallel Speech Recognition for Mobile Applications

    Directory of Open Access Journals (Sweden)

    LOJKA Martin

    2014-05-01

    Full Text Available In this paper, the server based solution of the multi-thread large vocabulary automatic speech recognition engine is described along with the Android OS and HTML5 practical application examples. The basic idea was to bring speech recognition available for full variety of applications for computers and especially for mobile devices. The speech recognition engine should be independent of commercial products and services (where the dictionary could not be modified. Using of third-party services could be also a security and privacy problem in specific applications, when the unsecured audio data could not be sent to uncontrolled environments (voice data transferred to servers around the globe. Using our experience with speech recognition applications, we have been able to construct a multi-thread speech recognition serverbased solution designed for simple applications interface (API to speech recognition engine modified to specific needs of particular application.

  18. Assessing the Performance of Automatic Speech Recognition Systems When Used by Native and Non-Native Speakers of Three Major Languages in Dictation Workflows

    DEFF Research Database (Denmark)

    Zapata, Julián; Kirkedal, Andreas Søeborg

    2015-01-01

    In this paper, we report on a two-part experiment aiming to assess and compare the performance of two types of automatic speech recognition (ASR) systems on two different computational platforms when used to augment dictation workflows. The experiment was performed with a sample of speakers...... of three major languages and with different linguistic profiles: non-native English speakers; non-native French speakers; and native Spanish speakers. The main objective of this experiment is to examine ASR performance in translation dictation (TD) and medical dictation (MD) workflows without manual...... transcription vs. with transcription. We discuss the advantages and drawbacks of a particular ASR approach in different computational platforms when used by various speakers of a given language, who may have different accents and levels of proficiency in that language, and who may have different levels...

  19. Speech recognition: Acoustic, phonetic and lexical

    Science.gov (United States)

    Zue, V. W.

    1985-10-01

    Our long-term research goal is the development and implementation of speaker-independent continuous speech recognition systems. It is our conviction that proper utilization of speech-specific knowledge is essential for advanced speech recognition systems. With this in mind, we have continued to make progress on the acquisition of acoustic-phonetic and lexical knowledge. We have completed the development of a continuous digit recognition system. The system was constructed to investigate the utilization of acoustic phonetic knowledge in a speech recognition system. Some of the significant development of this study includes a soft-failure procedure for lexical access, and the discovery of a set of acoustic-phonetic features for verification. We have completed a study of the constraints provided by lexical stress on word recognition. We found that lexical stress information alone can, on the average, reduce the number of word candidates from a large dictionary by more than 80%. In conjunction with this study, we successfully developed a system that automatically determines the stress pattern of a word from the acoustic signal.

  20. Phonetic Alphabet for Speech Recognition of Czech

    Directory of Open Access Journals (Sweden)

    J. Uhlir

    1997-12-01

    Full Text Available In the paper we introduce and discuss an alphabet that has been proposed for phonemicly oriented automatic speech recognition. The alphabet, denoted as a PAC (Phonetic Alphabet for Czech consists of 48 basic symbols that allow for distinguishing all major events occurring in spoken Czech language. The symbols can be used both for phonetic transcription of Czech texts as well as for labeling recorded speech signals. From practical reasons, the alphabet occurs in two versions; one utilizes Czech native characters and the other employs symbols similar to those used for English in the DARPA and NIST alphabets.

  1. Emotion Recognition using Speech Features

    CERN Document Server

    Rao, K Sreenivasa

    2013-01-01

    “Emotion Recognition Using Speech Features” covers emotion-specific features present in speech and discussion of suitable models for capturing emotion-specific information for distinguishing different emotions.  The content of this book is important for designing and developing  natural and sophisticated speech systems. Drs. Rao and Koolagudi lead a discussion of how emotion-specific information is embedded in speech and how to acquire emotion-specific knowledge using appropriate statistical models. Additionally, the authors provide information about using evidence derived from various features and models. The acquired emotion-specific knowledge is useful for synthesizing emotions. Discussion includes global and local prosodic features at syllable, word and phrase levels, helpful for capturing emotion-discriminative information; use of complementary evidences obtained from excitation sources, vocal tract systems and prosodic features in order to enhance the emotion recognition performance;  and pro...

  2. Time-expanded speech and speech recognition in older adults.

    Science.gov (United States)

    Vaughan, Nancy E; Furukawa, Izumi; Balasingam, Nirmala; Mortz, Margaret; Fausti, Stephen A

    2002-01-01

    Speech understanding deficits are common in older adults. In addition to hearing sensitivity, changes in certain cognitive functions may affect speech recognition. One such change that may impact the ability to follow a rapidly changing speech signal is processing speed. When speakers slow the rate of their speech naturally in order to speak clearly, speech recognition is improved. The acoustic characteristics of naturally slowed speech are of interest in developing time-expansion algorithms to improve speech recognition for older listeners. In this study, we tested younger normally hearing, older normally hearing, and older hearing-impaired listeners on time-expanded speech using increased duration and increased intensity of unvoiced consonants. Although all groups performed best on unprocessed speech, performance with processed speech was better with the consonant gain feature without time expansion in the noise condition and better at the slowest time-expanded rate in the quiet condition. The effects of signal processing on speech recognition are discussed. PMID:17642020

  3. Lattice Parsing for Speech Recognition

    OpenAIRE

    Chappelier, Jean-Cédric; Rajman, Martin; Aragües, Ramon; Rozenknop, Antoine

    1999-01-01

    A lot of work remains to be done in the domain of a better integration of speech recognition and language processing systems. This paper gives an overview of several strategies for integrating linguistic models into speech understanding systems and investigates several ways of producing sets of hypotheses that include more "semantic" variability than usual language models. The main goal is to present and demonstrate by actual experiments that sequential couplingmay be efficiently achieved byw...

  4. Discriminative learning for speech recognition

    CERN Document Server

    He, Xiadong

    2008-01-01

    In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminative learning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-functio

  5. Automatic Speech Segmentation Based on HMM

    OpenAIRE

    M. Kroul

    2007-01-01

    This contribution deals with the problem of automatic phoneme segmentation using HMMs. Automatization of speech segmentation task is important for applications, where large amount of data is needed to process, so manual segmentation is out of the question. In this paper we focus on automatic segmentation of recordings, which will be used for triphone synthesis unit database creation. For speech synthesis, the speech unit quality is a crucial aspect, so the maximal accuracy in segmentation is ...

  6. Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

    Directory of Open Access Journals (Sweden)

    Heracleous Panikos

    2007-01-01

    Full Text Available We present the use of stethoscope and silicon NAM (nonaudible murmur microphones in automatic speech recognition. NAM microphones are special acoustic sensors, which are attached behind the talker's ear and can capture not only normal (audible speech, but also very quietly uttered speech (nonaudible murmur. As a result, NAM microphones can be applied in automatic speech recognition systems when privacy is desired in human-machine communication. Moreover, NAM microphones show robustness against noise and they might be used in special systems (speech recognition, speech transform, etc. for sound-impaired people. Using adaptation techniques and a small amount of training data, we achieved for a 20 k dictation task a word accuracy for nonaudible murmur recognition in a clean environment. In this paper, we also investigate nonaudible murmur recognition in noisy environments and the effect of the Lombard reflex on nonaudible murmur recognition. We also propose three methods to integrate audible speech and nonaudible murmur recognition using a stethoscope NAM microphone with very promising results.

  7. Speech recognition: Acoustic, phonetic and lexical knowledge

    Science.gov (United States)

    Zue, V. W.

    1985-08-01

    During this reporting period we continued to make progress on the acquisition of acoustic-phonetic and lexical knowledge. We completed development of a continuous digit recognition system. The system was constructed to investigate the use of acoustic-phonetic knowledge in a speech recognition system. The significant achievements of this study include the development of a soft-failure procedure for lexical access and the discovery of a set of acoustic-phonetic features for verification. We completed a study of the constraints that lexical stress imposes on word recognition. We found that lexical stress information alone can, on the average, reduce the number of word candidates from a large dictionary by more than 80 percent. In conjunction with this study, we successfully developed a system that automatically determines the stress pattern of a word from the acoustic signal. We performed an acoustic study on the characteristics of nasal consonants and nasalized vowels. We have also developed recognition algorithms for nasal murmurs and nasalized vowels in continuous speech. We finished the preliminary development of a system that aligns a speech waveform with the corresponding phonetic transcription.

  8. The benefit obtained from visually displayed text from an automatic speech recognizer during listening to speech presented in noise

    NARCIS (Netherlands)

    Zekveld, A.A.; Kramer, S.E.; Kessens, J.M.; Vlaming, M.S.M.G.; Houtgast, T.

    2008-01-01

    OBJECTIVES: The aim of this study was to evaluate the benefit that listeners obtain from visually presented output from an automatic speech recognition (ASR) system during listening to speech in noise. DESIGN: Auditory-alone and audiovisual speech reception thresholds (SRTs) were measured. The SRT i

  9. On speech recognition during anaesthesia

    DEFF Research Database (Denmark)

    Alapetite, Alexandre

    2007-01-01

    This PhD thesis in human-computer interfaces (informatics) studies the case of the anaesthesia record used during medical operations and the possibility to supplement it with speech recognition facilities. Problems and limitations have been identified with the traditional paper-based anaesthesia ...... accuracy. Finally, the last part of the thesis looks at the acceptance and success of a speech recognition system introduced in a Danish hospital to produce patient records.......This PhD thesis in human-computer interfaces (informatics) studies the case of the anaesthesia record used during medical operations and the possibility to supplement it with speech recognition facilities. Problems and limitations have been identified with the traditional paper-based anaesthesia...... inaccuracies in the anaesthesia record. Supplementing the electronic anaesthesia record interface with speech input facilities is proposed as one possible solution to a part of the problem. The testing of the various hypotheses has involved the development of a prototype of an electronic anaesthesia record...

  10. Automatic Number Plate Recognition System

    OpenAIRE

    Rajshree Dhruw; Dharmendra Roy

    2014-01-01

    Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their license number. The objective is to design an efficient automatic authorized vehicle identification system by using the Indian vehicle number plate. In this paper we discus different methodology for number plate localization, character segmentation & recognition of the number plate. The system is mainly applicable for non standard Indian number plates by recognizing...

  11. A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

    Directory of Open Access Journals (Sweden)

    Chenchen Huang

    2014-01-01

    Full Text Available Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

  12. On speech recognition during anaesthesia

    OpenAIRE

    Alapetite, Alexandre

    2007-01-01

    This PhD thesis in human-computer interfaces (HCI, informatics) studies the case of the anaesthesia record used during medical operations and the possibility to supplement it with speech recognition facilities. Problems and limitations have been identified with the traditional paper-based anaesthesia record, but also with newer electronic versions, in particular ergonomic issues and the fact that anaesthesiologists tend to postpone the registration of the medications and other events during b...

  13. Emotion Recognition from Persian Speech with Neural Network

    Directory of Open Access Journals (Sweden)

    Mina Hamidi

    2012-10-01

    Full Text Available In this paper, we report an effort towards automatic recognition of emotional states from continuousPersian speech. Due to the unavailability of appropriate database in the Persian language for emotionrecognition, at first, we built a database of emotional speech in Persian. This database consists of 2400wave clips modulated with anger, disgust, fear, sadness, happiness and normal emotions. Then we extractprosodic features, including features related to the pitch, intensity and global characteristics of the speechsignal. Finally, we applied neural networks for automatic recognition of emotion. The resulting averageaccuracy was about 78%.

  14. Automatic Licenses Plate Recognition

    OpenAIRE

    Ronak P Patel; Narendra M Patel; Keyur Brahmbhatt

    2013-01-01

    This paper describes the Smart Vehicle Screening System, which can be installed into a tollboothfor automated recognition of vehicle license plate information using a photograph of a vehicle. An automatedsystem could then be implemented to control the payment of fees, parking areas, highways, bridges ortunnels, etc. This paper contains new algorithm for recognition number plate using Morphological operation,Thresholding operation, Edge detection, Bounding box analysis for number plate extract...

  15. Comparative wavelet, PLP, and LPC speech recognition techniques on the Hindi speech digits database

    Science.gov (United States)

    Mishra, A. N.; Shrotriya, M. C.; Sharan, S. N.

    2010-02-01

    In view of the growing use of automatic speech recognition in the modern society, we study various alternative representations of the speech signal that have the potential to contribute to the improvement of the recognition performance. In this paper wavelet based features using different wavelets are used for Hindi digits recognition. The recognition performance of these features has been compared with Linear Prediction Coefficients (LPC) and Perceptual Linear Prediction (PLP) features. All features have been tested using Hidden Markov Model (HMM) based classifier for speaker independent Hindi digits recognition. The recognition performance of PLP features is11.3% better than LPC features. The recognition performance with db10 features has shown a further improvement of 12.55% over PLP features. The recognition performance with db10 is best among all wavelet based features.

  16. Automatic pattern recognition

    OpenAIRE

    Petheram, R.J.

    1989-01-01

    In this thesis the author presents a new method for the location, extraction and normalisation of discrete objects found in digital images. The extraction is by means of sub-pixcel contour following around the object. The normalisation obtains and removes the information concerning size, orientation and location of the object within an image. Analyses of the results are carried out to determine the confidence in recognition of patterns, and methods of cross correlation of object descriptions ...

  17. GesRec3D: a real-time coded gesture-to-speech system with automatic segmentation and recognition thresholding using dissimilarity measures

    OpenAIRE

    Craven, Michael P; Curtis, K. Mervyn

    2004-01-01

    A complete microcomputer system is described, GesRec3D, which facilitates the data acquisition, segmentation, learning, and recognition of 3-Dimensional arm gestures, with application as a Augmentative and Alternative Communication (AAC) aid for people with motor and speech disability. The gesture data is acquired from a Polhemus electro-magnetic tracker system, with sensors attached to the finger, wrist and elbow of one arm. Coded gestures are linked to user-defined text, to be spoken by a t...

  18. Automatic speech signal segmentation based on the innovation adaptive filter

    Directory of Open Access Journals (Sweden)

    Makowski Ryszard

    2014-06-01

    Full Text Available Speech segmentation is an essential stage in designing automatic speech recognition systems and one can find several algorithms proposed in the literature. It is a difficult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006, and it is a modified version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefficients of an innovation adaptive filter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics defined on the mel spectrum determined from the reflection coefficients. The paper presents the structure of the algorithm, defines its properties, lists parameter values, describes detection efficiency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.

  19. Comparison of Speech Features on the Speech Recognition Task

    Directory of Open Access Journals (Sweden)

    Iosif Mporas

    2007-01-01

    Full Text Available In the present work we overview some recently proposed discrete Fourier transform (DFT- and discrete wavelet packet transform (DWPT-based speech parameterization methods and evaluate their performance on the speech recognition task. Specifically, in order to assess the practical value of these less studied speech parameterization methods, we evaluate them in a common experimental setup and compare their performance against traditional techniques, such as the Mel-frequency cepstral coefficients (MFCC and perceptual linear predictive (PLP cepstral coefficients which presently dominate the speech recognition field. In particular, utilizing the well established TIMIT speech corpus and employing the Sphinx-III speech recognizer, we present comparative results of 8 different speech parameterization techniques.

  20. Automatic Identification of Silence, Unvoiced and Voiced Chunks in Speech

    Directory of Open Access Journals (Sweden)

    Poonam Sharma

    2013-05-01

    Full Text Available The objective of this work is to automatically seg ment the speech signal into silence, voiced and unvoiced regions which are very beneficial in incre asing the accuracy and performance of recognition systems. Proposed algorithm is based on three important characteristics of speech signal namely Zero Crossing Rate, Short Time Energy and Fundamental Frequency. The performance of the proposed algorithm is evaluated using the data collected from four different speakers and an overall accuracy of 96.61 % is achi eved.

  1. Physics of Automatic Target Recognition

    CERN Document Server

    Sadjadi, Firooz

    2007-01-01

    Physics of Automatic Target Recognition addresses the fundamental physical bases of sensing, and information extraction in the state-of-the art automatic target recognition field. It explores both passive and active multispectral sensing, polarimetric diversity, complex signature exploitation, sensor and processing adaptation, transformation of electromagnetic and acoustic waves in their interactions with targets, background clutter, transmission media, and sensing elements. The general inverse scattering, and advanced signal processing techniques and scientific evaluation methodologies being used in this multi disciplinary field will be part of this exposition. The issues of modeling of target signatures in various spectral modalities, LADAR, IR, SAR, high resolution radar, acoustic, seismic, visible, hyperspectral, in diverse geometric aspects will be addressed. The methods for signal processing and classification will cover concepts such as sensor adaptive and artificial neural networks, time reversal filt...

  2. A Statistical Approach to Automatic Speech Summarization

    Science.gov (United States)

    Hori, Chiori; Furui, Sadaoki; Malkin, Rob; Yu, Hua; Waibel, Alex

    2003-12-01

    This paper proposes a statistical approach to automatic speech summarization. In our method, a set of words maximizing a summarization score indicating the appropriateness of summarization is extracted from automatically transcribed speech and then concatenated to create a summary. The extraction process is performed using a dynamic programming (DP) technique based on a target compression ratio. In this paper, we demonstrate how an English news broadcast transcribed by a speech recognizer is automatically summarized. We adapted our method, which was originally proposed for Japanese, to English by modifying the model for estimating word concatenation probabilities based on a dependency structure in the original speech given by a stochastic dependency context free grammar (SDCFG). We also propose a method of summarizing multiple utterances using a two-level DP technique. The automatically summarized sentences are evaluated by summarization accuracy based on a comparison with a manual summary of speech that has been correctly transcribed by human subjects. Our experimental results indicate that the method we propose can effectively extract relatively important information and remove redundant and irrelevant information from English news broadcasts.

  3. A Statistical Approach to Automatic Speech Summarization

    Directory of Open Access Journals (Sweden)

    Chiori Hori

    2003-02-01

    Full Text Available This paper proposes a statistical approach to automatic speech summarization. In our method, a set of words maximizing a summarization score indicating the appropriateness of summarization is extracted from automatically transcribed speech and then concatenated to create a summary. The extraction process is performed using a dynamic programming (DP technique based on a target compression ratio. In this paper, we demonstrate how an English news broadcast transcribed by a speech recognizer is automatically summarized. We adapted our method, which was originally proposed for Japanese, to English by modifying the model for estimating word concatenation probabilities based on a dependency structure in the original speech given by a stochastic dependency context free grammar (SDCFG. We also propose a method of summarizing multiple utterances using a two-level DP technique. The automatically summarized sentences are evaluated by summarization accuracy based on a comparison with a manual summary of speech that has been correctly transcribed by human subjects. Our experimental results indicate that the method we propose can effectively extract relatively important information and remove redundant and irrelevant information from English news broadcasts.

  4. Personality in speech assessment and automatic classification

    CERN Document Server

    Polzehl, Tim

    2015-01-01

    This work combines interdisciplinary knowledge and experience from research fields of psychology, linguistics, audio-processing, machine learning, and computer science. The work systematically explores a novel research topic devoted to automated modeling of personality expression from speech. For this aim, it introduces a novel personality assessment questionnaire and presents the results of extensive labeling sessions to annotate the speech data with personality assessments. It provides estimates of the Big 5 personality traits, i.e. openness, conscientiousness, extroversion, agreeableness, and neuroticism. Based on a database built on the questionnaire, the book presents models to tell apart different personality types or classes from speech automatically.

  5. Speech recognition systems on the Cell Broadband Engine

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Y; Jones, H; Vaidya, S; Perrone, M; Tydlitat, B; Nanda, A

    2007-04-20

    In this paper we describe our design, implementation, and first results of a prototype connected-phoneme-based speech recognition system on the Cell Broadband Engine{trademark} (Cell/B.E.). Automatic speech recognition decodes speech samples into plain text (other representations are possible) and must process samples at real-time rates. Fortunately, the computational tasks involved in this pipeline are highly data-parallel and can receive significant hardware acceleration from vector-streaming architectures such as the Cell/B.E. Identifying and exploiting these parallelism opportunities is challenging, but also critical to improving system performance. We observed, from our initial performance timings, that a single Cell/B.E. processor can recognize speech from thousands of simultaneous voice channels in real time--a channel density that is orders-of-magnitude greater than the capacity of existing software speech recognizers based on CPUs (central processing units). This result emphasizes the potential for Cell/B.E.-based speech recognition and will likely lead to the future development of production speech systems using Cell/B.E. clusters.

  6. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

    Directory of Open Access Journals (Sweden)

    Pavol Partila

    2015-01-01

    Full Text Available The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

  7. Speech Recognition in Natural Background Noise

    OpenAIRE

    Julien Meyer; Laure Dentel; Fanny Meunier

    2013-01-01

    In the real world, human speech recognition nearly always involves listening in background noise. The impact of such noise on speech signals and on intelligibility performance increases with the separation of the listener from the speaker. The present behavioral experiment provides an overview of the effects of such acoustic disturbances on speech perception in conditions approaching ecologically valid contexts. We analysed the intelligibility loss in spoken word lists with increasing listene...

  8. PCA-Based Speech Enhancement for Distorted Speech Recognition

    Directory of Open Access Journals (Sweden)

    Tetsuya Takiguchi

    2007-09-01

    Full Text Available We investigated a robust speech feature extraction method using kernel PCA (Principal Component Analysis for distorted speech recognition. Kernel PCA has been suggested for various image processing tasks requiring an image model, such as denoising, where a noise-free image is constructed from a noisy input image. Much research for robust speech feature extraction has been done, but it remains difficult to completely remove additive or convolution noise (distortion. The most commonly used noise-removal techniques are based on the spectraldomain operation, and then for speech recognition, the MFCC (Mel Frequency Cepstral Coefficient is computed, where DCT (Discrete Cosine Transform is applied to the mel-scale filter bank output. This paper describes a new PCA-based speech enhancement algorithm using kernel PCA instead of DCT, where the main speech element is projected onto low-order features, while the noise or distortion element is projected onto high-order features. Its effectiveness is confirmed by word recognition experiments on distorted speech.

  9. Auditory—Spectrum Quantization Based Speech Recognition

    Institute of Scientific and Technical Information of China (English)

    WuYuanqing; HaoJie; 等

    1997-01-01

    Based on the analysis of the physiological and psychological characteristics of human auditory system[1],we can classify human auditory process into two hearing modes:active one and passive one.A novel approach of robust speech recognition,Auditory-spectrum Quantization Based Speech Recognition(AQBSR),is proposed.In this method,we intend to simulate human active hearing mode and locate the effective areas of speech signals in temporal domain and in frequency domain.Adaptive filter banks are used in place of fixed-band filters to extract feature parameters.The effective speech components and their corresponding frequency areas of each word in the vocabulary can be found out during training.In recognition stage,comparison between the unknown sound and the current template is maintained only in the effective areas of the template word.The control experiments show that the AQ BSR method is more robust than traditional systems.

  10. Can automatic speech transcripts be used for large scale TV stream description and structuring?

    OpenAIRE

    Guinaudeau, Camille; Gravier, Guillaume; Sébillot, Pascale

    2009-01-01

    International audience The increasing quantity of TV material requires methods to help users navigate such data streams. Automatically associating a short textual description with each program in a stream, is a first stage to navigating or structuring tasks. Speech contained in TV broadcasts--accessible by means of automatic speech recognition systems in the absence of closed caption--is a highly valuable semantic clue that might be used to link existing textual description such as program...

  11. Hidden neural networks: application to speech recognition

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1998-01-01

    We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...... (HNNs) with much fewer parameters than conventional HMMs and other hybrids can obtain comparable performance, and for the broad class task it is illustrated how the HNN can be applied as a purely transition based system, where acoustic context dependent transition probabilities are estimated by neural...... networks...

  12. Objective Gender and Age Recognition from Speech Sentences

    Directory of Open Access Journals (Sweden)

    Fatima K. Faek

    2015-10-01

    Full Text Available In this work, an automatic gender and age recognizer from speech is investigated. The relevant features to gender recognition are selected from the first four formant frequencies and twelve MFCCs and feed the SVM classifier. While the relevant features to age has been used with k-NN classifier for the age recognizer model, using MATLAB as a simulation tool. A special selection of robust features is used in this work to improve the results of the gender and age classifiers based on the frequency range that the feature represents. The gender and age classification algorithms are evaluated using 114 (clean and noisy speech samples uttered in Kurdish language. The model of two classes (adult males and adult females gender recognition, reached 96% recognition accuracy. While for three categories classification (adult males, adult females, and children, the model achieved 94% recognition accuracy. For the age recognition model, seven groups according to their ages are categorized. The model performance after selecting the relevant features to age achieved 75.3%. For further improvement a de-noising technique is used with the noisy speech signals, followed by selecting the proper features that are affected by the de-noising process and result in 81.44% recognition accuracy.

  13. Emotion recognition from speech: tools and challenges

    Science.gov (United States)

    Al-Talabani, Abdulbasit; Sellahewa, Harin; Jassim, Sabah A.

    2015-05-01

    Human emotion recognition from speech is studied frequently for its importance in many applications, e.g. human-computer interaction. There is a wide diversity and non-agreement about the basic emotion or emotion-related states on one hand and about where the emotion related information lies in the speech signal on the other side. These diversities motivate our investigations into extracting Meta-features using the PCA approach, or using a non-adaptive random projection RP, which significantly reduce the large dimensional speech feature vectors that may contain a wide range of emotion related information. Subsets of Meta-features are fused to increase the performance of the recognition model that adopts the score-based LDC classifier. We shall demonstrate that our scheme outperform the state of the art results when tested on non-prompted databases or acted databases (i.e. when subjects act specific emotions while uttering a sentence). However, the huge gap between accuracy rates achieved on the different types of datasets of speech raises questions about the way emotions modulate the speech. In particular we shall argue that emotion recognition from speech should not be dealt with as a classification problem. We shall demonstrate the presence of a spectrum of different emotions in the same speech portion especially in the non-prompted data sets, which tends to be more "natural" than the acted datasets where the subjects attempt to suppress all but one emotion.

  14. Testing for robust speech recognition performance

    Science.gov (United States)

    Simpson, C. A.; Moore, C. A.; Ruth, J. C.

    Results are reported from two studies which evaluated speaker-dependent connected-speech template-matching algorithms. One study examined the recognition performance for vocabularies spoken within a spacesuit. Two token vocabularies were used that were recorded in different noise levels. The second study evaluated the rejection accuracy for two commercial speech recognizers. The spoken test tokens were variations on a single word. The tests underscored the inferiority of speech recognizers relative to the human capability for discerning among phonetically different words. However, one commercial recognizer exhibited over 96-percent rejection accuracy in a noisy environment.

  15. Novel acoustic features for speech emotion recognition

    Institute of Scientific and Technical Information of China (English)

    ROH Yong-Wan; KIM Dong-Ju; LEE Woo-Seok; HONG Kwang-Seok

    2009-01-01

    This paper focuses on acoustic features that effectively improve the recognition of emotion in human speech. The novel features in this paper are based on spectral-based entropy parameters such as fast Fourier transform (FFT) spectral entropy, delta FFT spectral entropy, Mel-frequency filter bank (MFB)spectral entropy, and Delta MFB spectral entropy. Spectral-based entropy features are simple. They reflect frequency characteristic and changing characteristic in frequency of speech. We implement an emotion rejection module using the probability distribution of recognized-scores and rejected-scores.This reduces the false recognition rate to improve overall performance. Recognized-scores and rejected-scores refer to probabilities of recognized and rejected emotion recognition results, respectively.These scores are first obtained from a pattern recognition procedure. The pattern recognition phase uses the Gaussian mixture model (GMM). We classify the four emotional states as anger, sadness,happiness and neutrality. The proposed method is evaluated using 45 sentences in each emotion for 30 subjects, 15 males and 15 females. Experimental results show that the proposed method is superior to the existing emotion recognition methods based on GMM using energy, Zero Crossing Rate (ZCR),linear prediction coefficient (LPC), and pitch parameters. We demonstrate the effectiveness of the proposed approach. One of the proposed features, combined MFB and delta MFB spectral entropy improves performance approximately 10% compared to the existing feature parameters for speech emotion recognition methods. We demonstrate a 4% performance improvement in the applied emotion rejection with low confidence score.

  16. Why Don't You See What I Mean? Prospects and Limitations of Current Automatic Sign Recognition Research

    Science.gov (United States)

    ten Holt, Gineke; Hendriks, Petra; Andriga, Tjeerd

    2006-01-01

    This article presents an overview of current automatic sign recognition research. A review of recent studies, as well as on our own research, has identified several problem areas that hamper successful sign recognition by a computer. Some of these problems are shared with automatic speech recognition, whereas others seem to be unique to automatic…

  17. Connected digit speech recognition system for Malayalam language

    Indian Academy of Sciences (India)

    Cini Kurian; Kannan Balakrishnan

    2013-12-01

    A connected digit speech recognition is important in many applications such as automated banking system, catalogue-dialing, automatic data entry, automated banking system, etc. This paper presents an optimum speaker-independent connected digit recognizer for Malayalam language. The system employs Perceptual Linear Predictive (PLP) cepstral coefficient for speech parameterization and continuous density Hidden Markov Model (HMM) in the recognition process. Viterbi algorithm is used for decoding. The training data base has the utterance of 21 speakers from the age group of 20 to 40 years and the sound is recorded in the normal office environment where each speaker is asked to read 20 set of continuous digits. The system obtained an accuracy of 99.5 % with the unseen data.

  18. Employment of Spectral Voicing Information for Speech and Speaker Recognition in Noisy Conditions

    OpenAIRE

    Jan&#;ovič, Peter; Köküer, M&#;nevver

    2008-01-01

    This chapter described our recent research on representation and modelling of speech signals for automatic speech and speaker recognition in noisy conditions. The chapter consisted of three parts. In the first part, we presented a novel method for estimation of the voicing information of speech spectra in the presence of noise. The presented method is based on calculating a similarity between the shape of signal short-term spectrum and the spectrum of the frame-analysis window. It does not re...

  19. Human and automatic speaker recognition over telecommunication channels

    CERN Document Server

    Fernández Gallardo, Laura

    2016-01-01

    This work addresses the evaluation of the human and the automatic speaker recognition performances under different channel distortions caused by bandwidth limitation, codecs, and electro-acoustic user interfaces, among other impairments. Its main contribution is the demonstration of the benefits of communication channels of extended bandwidth, together with an insight into how speaker-specific characteristics of speech are preserved through different transmissions. It provides sufficient motivation for considering speaker recognition as a criterion for the migration from narrowband to enhanced bandwidths, such as wideband and super-wideband.

  20. Effects of Cognitive Load on Speech Recognition

    Science.gov (United States)

    Mattys, Sven L.; Wiget, Lukas

    2011-01-01

    The effect of cognitive load (CL) on speech recognition has received little attention despite the prevalence of CL in everyday life, e.g., dual-tasking. To assess the effect of CL on the interaction between lexically-mediated and acoustically-mediated processes, we measured the magnitude of the "Ganong effect" (i.e., lexical bias on phoneme…

  1. Bimodal Emotion Recognition from Speech and Text

    Directory of Open Access Journals (Sweden)

    Weilin Ye

    2014-01-01

    Full Text Available This paper presents an approach to emotion recognition from speech signals and textual content. In the analysis of speech signals, thirty-seven acoustic features are extracted from the speech input. Two different classifiers Support Vector Machines (SVMs and BP neural network are adopted to classify the emotional states. In text analysis, we use the two-step classification method to recognize the emotional states. The final emotional state is determined based on the emotion outputs from the acoustic and textual analyses. In this paper we have two parallel classifiers for acoustic information and two serial classifiers for textual information, and a final decision is made by combing these classifiers in decision level fusion. Experimental results show that the emotion recognition accuracy of the integrated system is better than that of either of the two individual approaches.

  2. Practical automatic Arabic license plate recognition system

    Science.gov (United States)

    Mohammad, Khader; Agaian, Sos; Saleh, Hani

    2011-02-01

    Since 1970's, the need of an automatic license plate recognition system, sometimes referred as Automatic License Plate Recognition system, has been increasing. A license plate recognition system is an automatic system that is able to recognize a license plate number, extracted from image sensors. In specific, Automatic License Plate Recognition systems are being used in conjunction with various transportation systems in application areas such as law enforcement (e.g. speed limit enforcement) and commercial usages such as parking enforcement and automatic toll payment private and public entrances, border control, theft and vandalism control. Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. [License plate detection using cluster run length smoothing algorithm ].Generally, an automatic license plate localization and recognition system is made up of three modules; license plate localization, character segmentation and optical character recognition modules. This paper presents an Arabic license plate recognition system that is insensitive to character size, font, shape and orientation with extremely high accuracy rate. The proposed system is based on a combination of enhancement, license plate localization, morphological processing, and feature vector extraction using the Haar transform. The performance of the system is fast due to classification of alphabet and numerals based on the license plate organization. Experimental results for license plates of two different Arab countries show an average of 99 % successful license plate localization and recognition in a total of more than 20 different images captured from a complex outdoor environment. The results run times takes less time compared to conventional and many states of art methods.

  3. The application of manifold based visual speech units for visual speech recognition

    OpenAIRE

    Yu, Dahai

    2008-01-01

    This dissertation presents a new learning-based representation that is referred to as a Visual Speech Unit for visual speech recognition (VSR). The automated recognition of human speech using only features from the visual domain has become a significant research topic that plays an essential role in the development of many multimedia systems such as audio visual speech recognition(AVSR), mobile phone applications, human-computer interaction (HCI) and sign language recognition. The inclusio...

  4. Novel acoustic features for speech emotion recognition

    Institute of Scientific and Technical Information of China (English)

    ROH; Yong-Wan; KIM; Dong-Ju; LEE; Woo-Seok; HONG; Kwang-Seok

    2009-01-01

    This paper focuses on acoustic features that effectively improve the recognition of emotion in human speech.The novel features in this paper are based on spectral-based entropy parameters such as fast Fourier transform(FFT) spectral entropy,delta FFT spectral entropy,Mel-frequency filter bank(MFB) spectral entropy,and Delta MFB spectral entropy.Spectral-based entropy features are simple.They reflect frequency characteristic and changing characteristic in frequency of speech.We implement an emotion rejection module using the probability distribution of recognized-scores and rejected-scores.This reduces the false recognition rate to improve overall performance.Recognized-scores and rejected-scores refer to probabilities of recognized and rejected emotion recognition results,respectively.These scores are first obtained from a pattern recognition procedure.The pattern recognition phase uses the Gaussian mixture model(GMM).We classify the four emotional states as anger,sadness,happiness and neutrality.The proposed method is evaluated using 45 sentences in each emotion for 30 subjects,15 males and 15 females.Experimental results show that the proposed method is superior to the existing emotion recognition methods based on GMM using energy,Zero Crossing Rate(ZCR),linear prediction coefficient(LPC),and pitch parameters.We demonstrate the effectiveness of the proposed approach.One of the proposed features,combined MFB and delta MFB spectral entropy improves performance approximately 10% compared to the existing feature parameters for speech emotion recognition methods.We demonstrate a 4% performance improvement in the applied emotion rejection with low confidence score.

  5. Phoneme fuzzy characterization in speech recognition systems

    Science.gov (United States)

    Beritelli, Francesco; Borrometi, Luca; Cuce, Antonino

    1997-10-01

    The acoustic approach to speech recognition has an important advantage compared with pattern recognition approach: it presents a lower complexity because it doesn't require explicit structures such as the hidden Markov model. In this work, we show how to characterize some phonetic classes of the Italian language in order to obtain a speaker and vocabulary independent speech recognition system. A phonetic data base is carried out with 200 continuous speech sentences of 12 speakers, 6 females and 6 males. The sentences are sampled at 8000 Hz and manual labelled with Asystem Sound Impression Software to obtain about 1600 units. We analyzed several speech parameters such as formants, LPC and reflection coefficients, energy, normal/differential zero crossing rate, cepstral and autocorrelation coefficients. The aim is the achievement of a phonetic recognizer to facilitate the so- called lexical access problem, that is to decode phonetic units into complete sense word strings. The knowledge is supplied to the recognizer in terms of fuzzy systems. The utilized software is called adaptive fuzzy modeler and it belongs to the rule generator family. A procedure has been implemented to integrate in the fuzzy system an 'expert' knowledge in order to obtain significant improvements in the recognition accuracy. Up to this point the tests show a recognition rate of 92% for the vocal class, 89% for the fricatives class and 94% for the nasal class, utilizing 1000 phonemes in phase of learning and 600 phonemes in phase of testing. Our intention is to complete the fuzzy recognizer extending this work to the other phonetic classes.

  6. Merge-Weighted Dynamic Time Warping for Speech Recognition

    Institute of Scientific and Technical Information of China (English)

    张湘莉兰; 骆志刚; 李明

    2014-01-01

    Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, language-independent (LI) with lightweight speaker-dependent (SD) automatic speech recognition (ASR) is a convenient option to solve the problem. The dynamic time warping (DTW) algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several limitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping (MWDTW) algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model (HMM) significantly, and is also six times faster than DTW overall.

  7. A Dialectal Chinese Speech Recognition Framework

    Institute of Scientific and Technical Information of China (English)

    Jing Li; Thomas Fang Zheng; William Byrne; Dan Jurafsky

    2006-01-01

    A framework for dialectal Chinese speech recognition is proposed and studied, in which a relatively small dialectal Chinese (or in other words Chinese influenced by the native dialect) speech corpus and dialect-related knowledge are adopted to transform a standard Chinese (or Putonghua, abbreviated as PTH) speech recognizer into a dialectal Chinese speech recognizer. Two kinds of knowledge sources are explored: one is expert knowledge and the other is a small dialectal Chinese corpus. These knowledge sources provide information at four levels: phonetic level, lexicon level, language level,and acoustic decoder level. This paper takes Wu dialectal Chinese (WDC) as an example target language. The goal is to establish a WDC speech recognizer from an existing PTH speech recognizer based on the Initial-Final structure of the Chinese language and a study of how dialectal Chinese speakers speak Putonghua. The authors propose to use contextindependent PTH-IF mappings (where IF means either a Chinese Initial or a Chinese Final), context-independent WDC-IF mappings, and syllable-dependent WDC-IF mappings (obtained from either experts or data), and combine them with the supervised maximum likelihood linear regression (MLLR) acoustic model adaptation method. To reduce the size of the multipronunciation lexicon introduced by the IF mappings, which might also enlarge the lexicon confusion and hence lead to the performance degradation, a Multi-Pronunciation Expansion (MPE) method based on the accumulated uni-gram probability (AUP) is proposed. In addition, some commonly used WDC words are selected and added to the lexicon. Compared with the original PTH speech recognizer, the resulting WDC speech recognizer achieves 10-18% absolute Character Error Rate (CER) reduction when recognizing WDC, with only a 0.62% CER increase when recognizing PTH. The proposed framework and methods are expected to work not only for Wu dialectal Chinese but also for other dialectal Chinese languages and

  8. Speech Recognition Technology for Hearing Disabled Community

    Directory of Open Access Journals (Sweden)

    Tanvi Dua

    2014-09-01

    Full Text Available As the number of people with hearing disabilities are increasing significantly in the world, it is always required to use technology for filling the gap of communication between Deaf and Hearing communities. To fill this gap and to allow people with hearing disabilities to communicate this paper suggests a framework that contributes to the efficient integration of people with hearing disabilities. This paper presents a robust speech recognition system, which converts the continuous speech into text and image. The results are obtained with an accuracy of 95% with the small size vocabulary of 20 greeting sentences of continuous speech form tested in a speaker independent mode. In this testing phase all these continuous sentences were given as live input to the proposed system.

  9. Support vector machine for automatic pain recognition

    Science.gov (United States)

    Monwar, Md Maruf; Rezaei, Siamak

    2009-02-01

    Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

  10. Speech emotion recognition with unsupervised feature learning

    Institute of Scientific and Technical Information of China (English)

    Zheng-wei HUANG; Wen-tao XUE; Qi-rong MAO

    2015-01-01

    Emotion-based features are critical for achieving high performance in a speech emotion recognition (SER) system. In general, it is difficult to develop these features due to the ambiguity of the ground-truth. In this paper, we apply several unsupervised feature learning algorithms (including K-means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning task-related features by using unlabeled data, to speech emotion recognition. We then evaluate the performance of the proposed approach and present a detailed analysis of the effect of two important factors in the model setup, the content window size and the number of hidden layer nodes. Experimental results show that larger content windows and more hidden nodes contribute to higher performance. We also show that the two-layer network cannot explicitly improve performance compared to a single-layer network.

  11. Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

    Directory of Open Access Journals (Sweden)

    S. Hamidreza Kasaei

    2011-05-01

    Full Text Available In this paper, we propose design and initial implementation of a robust system which can automatically translates voice into text and text to sign language animations. Sign Language
    Translation Systems could significantly improve deaf lives especially in communications, exchange of information and employment of machine for translation conversations from one language to another has. Therefore, considering these points, it seems necessary to study the speech recognition. Usually, the voice recognition algorithms address three major challenges. The first is extracting feature form speech and the second is when limited sound gallery are available for recognition, and the final challenge is to improve speaker dependent to speaker independent voice recognition. Extracting feature form speech is an important stage in our method. Different procedures are available for extracting feature form speech. One of the commonest of which used in speech
    recognition systems is Mel-Frequency Cepstral Coefficients (MFCCs. The algorithm starts with preprocessing and signal conditioning. Next extracting feature form speech using Cepstral coefficients will be done. Then the result of this process sends to segmentation part. Finally recognition part recognizes the words and then converting word recognized to facial animation. The project is still in progress and some new interesting methods are described in the current report.

  12. Automatic recognition of element classes and boundaries in the birdsong with variable sequences

    OpenAIRE

    Koumura, Takuya; Okanoya, Kazuo

    2016-01-01

    Researches on sequential vocalization often require analysis of vocalizations in long continuous sounds. In such studies as developmental ones or studies across generations in which days or months of vocalizations must be analyzed, methods for automatic recognition would be strongly desired. Although methods for automatic speech recognition for application purposes have been intensively studied, blindly applying them for biological purposes may not be an optimal solution. This is because, unl...

  13. Compact Acoustic Models for Embedded Speech Recognition

    Directory of Open Access Journals (Sweden)

    Christophe Lévy

    2009-01-01

    Full Text Available Speech recognition applications are known to require a significant amount of resources. However, embedded speech recognition only authorizes few KB of memory, few MIPS, and small amount of training data. In order to fit the resource constraints of embedded applications, an approach based on a semicontinuous HMM system using state-independent acoustic modelling is proposed. A transformation is computed and applied to the global model in order to obtain each HMM state-dependent probability density functions, authorizing to store only the transformation parameters. This approach is evaluated on two tasks: digit and voice-command recognition. A fast adaptation technique of acoustic models is also proposed. In order to significantly reduce computational costs, the adaptation is performed only on the global model (using related speaker recognition adaptation techniques with no need for state-dependent data. The whole approach results in a relative gain of more than 20% compared to a basic HMM-based system fitting the constraints.

  14. Improved Open-Microphone Speech Recognition

    Science.gov (United States)

    Abrash, Victor

    2002-01-01

    Many current and future NASA missions make extreme demands on mission personnel both in terms of work load and in performing under difficult environmental conditions. In situations where hands are impeded or needed for other tasks, eyes are busy attending to the environment, or tasks are sufficiently complex that ease of use of the interface becomes critical, spoken natural language dialog systems offer unique input and output modalities that can improve efficiency and safety. They also offer new capabilities that would not otherwise be available. For example, many NASA applications require astronauts to use computers in micro-gravity or while wearing space suits. Under these circumstances, command and control systems that allow users to issue commands or enter data in hands-and eyes-busy situations become critical. Speech recognition technology designed for current commercial applications limits the performance of the open-ended state-of-the-art dialog systems being developed at NASA. For example, today's recognition systems typically listen to user input only during short segments of the dialog, and user input outside of these short time windows is lost. Mistakes detecting the start and end times of user utterances can lead to mistakes in the recognition output, and the dialog system as a whole has no way to recover from this, or any other, recognition error. Systems also often require the user to signal when that user is going to speak, which is impractical in a hands-free environment, or only allow a system-initiated dialog requiring the user to speak immediately following a system prompt. In this project, SRI has developed software to enable speech recognition in a hands-free, open-microphone environment, eliminating the need for a push-to-talk button or other signaling mechanism. The software continuously captures a user's speech and makes it available to one or more recognizers. By constantly monitoring and storing the audio stream, it provides the spoken

  15. Joint speech and spearker recognition using neural networks

    OpenAIRE

    Xue, Xiaoguo

    2013-01-01

    Speech is the main communication method between human beings. Since the time of the invention of the computer people have been trying to let the computer understand natural speech. Speech recognition is a technology which has close connections with computer science, signal processing, voice linguistics and intelligent systems. It has been a ”hot” subject not only in the field of research but also as a practical application. Especially in real life, speaker and speech recognition have been use...

  16. Speech recognition in natural background noise.

    Directory of Open Access Journals (Sweden)

    Julien Meyer

    Full Text Available In the real world, human speech recognition nearly always involves listening in background noise. The impact of such noise on speech signals and on intelligibility performance increases with the separation of the listener from the speaker. The present behavioral experiment provides an overview of the effects of such acoustic disturbances on speech perception in conditions approaching ecologically valid contexts. We analysed the intelligibility loss in spoken word lists with increasing listener-to-speaker distance in a typical low-level natural background noise. The noise was combined with the simple spherical amplitude attenuation due to distance, basically changing the signal-to-noise ratio (SNR. Therefore, our study draws attention to some of the most basic environmental constraints that have pervaded spoken communication throughout human history. We evaluated the ability of native French participants to recognize French monosyllabic words (spoken at 65.3 dB(A, reference at 1 meter at distances between 11 to 33 meters, which corresponded to the SNRs most revealing of the progressive effect of the selected natural noise (-8.8 dB to -18.4 dB. Our results showed that in such conditions, identity of vowels is mostly preserved, with the striking peculiarity of the absence of confusion in vowels. The results also confirmed the functional role of consonants during lexical identification. The extensive analysis of recognition scores, confusion patterns and associated acoustic cues revealed that sonorant, sibilant and burst properties were the most important parameters influencing phoneme recognition. . Altogether these analyses allowed us to extract a resistance scale from consonant recognition scores. We also identified specific perceptual consonant confusion groups depending of the place in the words (onset vs. coda. Finally our data suggested that listeners may access some acoustic cues of the CV transition, opening interesting perspectives for

  17. Comparison of Manual and Automatic Evaluation of Speech Recognition Threshold Using Mandarin Disyllabic Test%手动与自动取值对普通话双音节测试中言语识别阈的影响

    Institute of Scientific and Technical Information of China (English)

    郑中伟; 张华; 王越

    2014-01-01

    目的:比较手动测试取值与软件自动描记取值所得普通话双音节词汇表言语识别阈(speech recog-nition threshold ,SRT ),并探讨其临床应用的意义。方法选取128例正常人(听力正常组)以及57例从事噪声作业的工人(噪声组)为受试对象,均以普通话作为日常交流方式。应用丹麦Madsen Conera临床诊断听力计,采用难度等价性一致的一组双音节词汇表作为测试材料,测试初始给声强度为PT A上20 dB ,将手动取值获得的言语识别阈与Conera听力计工作软件自动生成的言语识别阈进行对比分析。结果听力正常组语频听阈均值7.63±5.78 dB HL ,自动取值所得SRT为7.84±3.98 dB HL ,手动取值所得SRT 为9.19±4.47 dB HL ;噪声组语频听阈均值27.18±19.13 dB HL ,自动取值所得SRT 为16.10±8.40 dB HL ,手动取值所得的SRT 为18.81±9.52 dB HL。两组手动取值所得的SRT值高于自动取值所得的SRT值(P<0.01)。结论自动取值SRT与手动取值SRT有差异,听力正常人的言语识别阈可用自动取值方法测试,便于听力正常人群的筛查;对听力障碍人群的SRT检查,更适合应用手动测试取值方法。%Objective To compare the results of manually -tested speech recognition threshold (SRT ) with automatically software -recorded SRT in the trial of Mandarin disyllabic test ,exploring the significance to the clini-cal applying .Methods 128 normal people of different ages without hearing loss and 57 workers exposed to noise in an automobile manufacturing was selected .These two group of volunteers speak mainly Mandarin in their daily life . MADSEN Conera (Danmark) clinical audiometr was applied .A group of double syllable word list with the same dif-ficulty of equivalence was used as test material .The initial presentation level was 20 dB HL higher than PTA .Then compared the results of manually -tested SRT with

  18. Automatic modulation recognition of communication signals

    CERN Document Server

    Azzouz, Elsayed Elsayed

    1996-01-01

    Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a cr...

  19. Towards automatic forensic face recognition

    NARCIS (Netherlands)

    Ali, Tauseef; Spreeuwers, Luuk; Veldhuis, Raymond

    2011-01-01

    In this paper we present a methodology and experimental results for evidence evaluation in the context of forensic face recognition. In forensic applications, the matching score (hereafter referred to as similarity score) from a biometric system must be represented as a Likelihood Ratio (LR). In our

  20. Performance of current models of speech recognition and resulting challenges

    OpenAIRE

    Schubotz, Wiebke

    2015-01-01

    Speech is usually perceived in background noise (masker) that can severely hamper its recognition. Nevertheless, there are mechanisms that enable speech recognition even in difficult listening conditions. Some of them, such as e.g., the combination of across-frequency information or binaural cues, are studied in this dissertation. Moreover, masking aspects such as energetic, amplitude modulation or informational masking are considered. Speech recognition in complex maskers is investigated tha...

  1. Speech Recognition Technology Applied to Intelligent Mobile Navigation System

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The capability of human-computer interaction reflects the intelligent degree of mobile navigation system.The navigation data and functions of mobile navigation system are divided into system commands and non-system commands in this paper.And then a group of speech commands are Abstracted.This paper applies speech recognition technology to intelligent mobile navigation system to process speech commands and does some deep research on the integration of speech recognition technology with mobile navigation system.The navigation operation can be performed by speech commands,which makes human-computer interaction easy during navigation.Speech command interface of navigation system is implemented by Dutty ++ Software,which is based on speech recognition system -Via Voice of IBM.Through navigation experiments,navigation can be done almost without keyboard,which proved that human-computer interaction is very convenient by speech commands and the reliability is also higher.

  2. Post-editing through Speech Recognition

    DEFF Research Database (Denmark)

    Mesa-Lao, Bartolomé

    recognition is gaining momentum, it seems reasonable to explore the interplay between both fields in a feasibility study. In the context of machine-aided human translation (MAHT), different scenarios have been investigated where human translators interact with a computer through a variety of input modalities...... computer-aided translation workbenches in the market (i.e. MemoQ) together with one of the most well-known ASR packages (i.e. Dragon Naturally Speaking from Nuance). Two data correction modes will be considered: a) keyboard vs. b) keyboard and speech combined. These two different ways of verifying...

  3. A pattern recognition based esophageal speech enhancement system

    Directory of Open Access Journals (Sweden)

    A.Mantilla‐Caeiros

    2010-04-01

    Full Text Available A system for improving the intelligibility and quality of alaryngeal speech based on the replacement of voiced segments ofalaryngeal speech with the equivalent segments of normal speech is proposed. To this end, the system proposed identifies thevoiced segments of the alaryngeal speech signal by using isolate speech recognition methods, and replaces them by theirequivalent voiced segments of normal speech, keeping the silence and unvoiced segments without change. Evaluation resultsusing objective and subjective evaluation methods show that the proposed system proposed provides a fairly goodimprovement of the quality and intelligibility of alaryngeal speech signals.

  4. Speech-based recognition of self-reported and observed emotion in a dimensional space

    NARCIS (Netherlands)

    Truong, Khiet P.; Leeuwen, van David A.; Jong, de Franciska M.G.

    2012-01-01

    The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two t

  5. Speech and audio processing for coding, enhancement and recognition

    CERN Document Server

    Togneri, Roberto; Narasimha, Madihally

    2015-01-01

    This book describes the basic principles underlying the generation, coding, transmission and enhancement of speech and audio signals, including advanced statistical and machine learning techniques for speech and speaker recognition with an overview of the key innovations in these areas. Key research undertaken in speech coding, speech enhancement, speech recognition, emotion recognition and speaker diarization are also presented, along with recent advances and new paradigms in these areas. ·         Offers readers a single-source reference on the significant applications of speech and audio processing to speech coding, speech enhancement and speech/speaker recognition. Enables readers involved in algorithm development and implementation issues for speech coding to understand the historical development and future challenges in speech coding research; ·         Discusses speech coding methods yielding bit-streams that are multi-rate and scalable for Voice-over-IP (VoIP) Networks; ·     �...

  6. How does real affect affect affect recognition in speech?

    NARCIS (Netherlands)

    Truong, Khiet Phuong

    2009-01-01

    The aim of the research described in this thesis was to develop speech-based affect recognition systems that can deal with spontaneous (‘real’) affect instead of acted affect. Several affect recognition experiments with spontaneous affective speech data were carried out to investigate what combinati

  7. Automatic TLI recognition system, user`s guide

    Energy Technology Data Exchange (ETDEWEB)

    Lassahn, G.D.

    1997-02-01

    This report describes how to use an automatic target recognition system (version 14). In separate volumes are a general description of the ATR system, Automatic TLI Recognition System, General Description, and a programmer`s manual, Automatic TLI Recognition System, Programmer`s Guide.

  8. Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

    Science.gov (United States)

    Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

    2016-10-01

    Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

  9. Automatic stereoscopic system for person recognition

    Science.gov (United States)

    Murynin, Alexander B.; Matveev, Ivan A.; Kuznetsov, Victor D.

    1999-06-01

    A biometric access control system based on identification of human face is presented. The system developed performs remote measurements of the necessary face features. Two different scenarios of the system behavior are implemented. The first one assumes the verification of personal data entered by visitor from console using keyboard or card reader. The system functions as an automatic checkpoint, that strictly controls access of different visitors. The other scenario makes it possible to identify visitors without any person identifier or pass. Only person biometrics are used to identify the visitor. The recognition system automatically finds necessary identification information preliminary stored in the database. Two laboratory models of recognition system were developed. The models are designed to use different information types and sources. In addition to stereoscopic images inputted to computer from cameras the models can use voice data and some person physical characteristics such as person's height, measured by imaging system.

  10. Automatic Gait Recognition by Symmetry Analysis

    OpenAIRE

    Hayfron-Acquah, James B.; Nixon, Mark S.; Carter, John N.

    2001-01-01

    We describe a new method for automatic gait recognition based on analysing the symmetry of human motion, by using the Generalised Symmetry Operator. This operator, rather than relying on the borders of a shape or on general appearance, locates features by their symmetrical properties. This approach is reinforced by the psychologists' view that gait is a symmetrical pattern of motion and by other works. We applied our new method to two different databases and derived gait signatures for silhou...

  11. Improving speech recognition on a mobile robot platform through the use of top-down visual queues

    OpenAIRE

    Ross, Robert; O'Donoghue, R. P. S.; O'Hare, G. M. P.

    2003-01-01

    In many real-world environments, Automatic Speech Recognition (ASR) technologies fail to provide adequate performance for applications such as human robot dialog. Despite substantial evidence that speech recognition in humans is performed in a top-down as well as bottom-up manner, ASR systems typically fail to capitalize on this, instead relying on a purely statistical, bottom up methodology. In this paper we advocate the use of a knowledge based approach to improving ASR in domains such as m...

  12. Part-of-Speech Enhanced Context Recognition

    DEFF Research Database (Denmark)

    Madsen, Rasmus Elsborg; Larsen, Jan; Hansen, Lars Kai

    2004-01-01

    Language independent `bag-of-words' representations are surprisingly efective for text classi¯cation. In this communi- cation our aim is to elucidate the synergy between language inde- pendent features and simple language model features. We consider term tag features estimated by a so-called part......-of-speech tagger. The feature sets are combined in an early binding design with an optimized binding coefficient that allows weighting of the relative variance contributions of the participating feature sets. With the combined features documents are classi¯ed using a latent semantic indexing representation...... and a probabilistic neural network classi- fier. Three medium size data-sets are analyzed and we find consis- tent synergy between the term and natural language features in all three sets for a range of training set sizes. The most significant en- hancement is found for small text databases where high recognition...

  13. Unification of automatic target tracking and automatic target recognition

    Science.gov (United States)

    Schachter, Bruce J.

    2014-06-01

    The subject being addressed is how an automatic target tracker (ATT) and an automatic target recognizer (ATR) can be fused together so tightly and so well that their distinctiveness becomes lost in the merger. This has historically not been the case outside of biology and a few academic papers. The biological model of ATT∪ATR arises from dynamic patterns of activity distributed across many neural circuits and structures (including retina). The information that the brain receives from the eyes is "old news" at the time that it receives it. The eyes and brain forecast a tracked object's future position, rather than relying on received retinal position. Anticipation of the next moment - building up a consistent perception - is accomplished under difficult conditions: motion (eyes, head, body, scene background, target) and processing limitations (neural noise, delays, eye jitter, distractions). Not only does the human vision system surmount these problems, but it has innate mechanisms to exploit motion in support of target detection and classification. Biological vision doesn't normally operate on snapshots. Feature extraction, detection and recognition are spatiotemporal. When vision is viewed as a spatiotemporal process, target detection, recognition, tracking, event detection and activity recognition, do not seem as distinct as they are in current ATT and ATR designs. They appear as similar mechanism taking place at varying time scales. A framework is provided for unifying ATT and ATR.

  14. Automatic Arabic Hand Written Text Recognition System

    Directory of Open Access Journals (Sweden)

    I. A. Jannoud

    2007-01-01

    Full Text Available Despite of the decent development of the pattern recognition science applications in the last decade of the twentieth century and this century, text recognition remains one of the most important problems in pattern recognition. To the best of our knowledge, little work has been done in the area of Arabic text recognition compared with those for Latin, Chins and Japanese text. The main difficulty encountered when dealing with Arabic text is the cursive nature of Arabic writing in both printed and handwritten forms. An Automatic Arabic Hand-Written Text Recognition (AHTR System is proposed. An efficient segmentation stage is required in order to divide a cursive word or sub-word into its constituting characters. After a word has been extracted from the scanned image, it is thinned and its base line is calculated by analysis of horizontal density histogram. The pattern is then followed through the base line and the segmentation points are detected. Thus after the segmentation stage, the cursive word is represented by a sequence of isolated characters. The recognition problem thus reduces to that of classifying each character. A set of features extracted from each individual characters. A minimum distance classifier is used. Some approaches are used for processing the characters and post processing added to enhance the results. Recognized characters will be appended directly to a word file which is editable form.

  15. Speech recognition algorithms based on weighted finite-state transducers

    CERN Document Server

    Hori, Takaaki

    2013-01-01

    This book introduces the theory, algorithms, and implementation techniques for efficient decoding in speech recognition mainly focusing on the Weighted Finite-State Transducer (WFST) approach. The decoding process for speech recognition is viewed as a search problem whose goal is to find a sequence of words that best matches an input speech signal. Since this process becomes computationally more expensive as the system vocabulary size increases, research has long been devoted to reducing the computational cost. Recently, the WFST approach has become an important state-of-the-art speech recogni

  16. An articulatorily constrained, maximum entropy approach to speech recognition and speech coding

    Energy Technology Data Exchange (ETDEWEB)

    Hogden, J.

    1996-12-31

    Hidden Markov models (HMM`s) are among the most popular tools for performing computer speech recognition. One of the primary reasons that HMM`s typically outperform other speech recognition techniques is that the parameters used for recognition are determined by the data, not by preconceived notions of what the parameters should be. This makes HMM`s better able to deal with intra- and inter-speaker variability despite the limited knowledge of how speech signals vary and despite the often limited ability to correctly formulate rules describing variability and invariance in speech. In fact, it is often the case that when HMM parameter values are constrained using the limited knowledge of speech, recognition performance decreases. However, the structure of an HMM has little in common with the mechanisms underlying speech production. Here, the author argues that by using probabilistic models that more accurately embody the process of speech production, he can create models that have all the advantages of HMM`s, but that should more accurately capture the statistical properties of real speech samples--presumably leading to more accurate speech recognition. The model he will discuss uses the fact that speech articulators move smoothly and continuously. Before discussing how to use articulatory constraints, he will give a brief description of HMM`s. This will allow him to highlight the similarities and differences between HMM`s and the proposed technique.

  17. Emotion Recognition from Speech Signals and Perception of Music

    OpenAIRE

    Fernandez Pradier, Melanie

    2011-01-01

    This thesis deals with emotion recognition from speech signals. The feature extraction step shall be improved by looking at the perception of music. In music theory, different pitch intervals (consonant, dissonant) and chords are believed to invoke different feelings in listeners. The question is whether there is a similar mechanism between perception of music and perception of emotional speech. Our research will follow three stages. First, the relationship between speech and music at segment...

  18. Effects of Speech Clarity on Recognition Memory for Spoken Sentences

    OpenAIRE

    Van Engen, Kristin J.; Bharath Chandrasekaran; Rajka Smiljanic

    2012-01-01

    Extensive research shows that inter-talker variability (i.e., changing the talker) affects recognition memory for speech signals. However, relatively little is known about the consequences of intra-talker variability (i.e. changes in speaking style within a talker) on the encoding of speech signals in memory. It is well established that speakers can modulate the characteristics of their own speech and produce a listener-oriented, intelligibility-enhancing speaking style in response to communi...

  19. SPEECH EMOTION RECOGNITION USING MODIFIED QUADRATIC DISCRIMINATION FUNCTION

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Quadratic Discrimination Function(QDF)is commonly used in speech emotion recognition,which proceeds on the premise that the input data is normal distribution.In this Paper,we propose a transformation to normalize the emotional features,then derivate a Modified QDF(MQDF) to speech emotion recognition.Features based on prosody and voice quality are extracted and Principal Component Analysis Neural Network (PCANN) is used to reduce dimension of the feature vectors.The results show that voice quality features are effective supplement for recognition.and the method in this paper could improve the recognition ratio effectively.

  20. Automatic discrimination between laughter and speech

    NARCIS (Netherlands)

    Truong, K.; Leeuwen, D. van

    2007-01-01

    Emotions can be recognized by audible paralinguistic cues in speech. By detecting these paralinguistic cues that can consist of laughter, a trembling voice, coughs, changes in the intonation contour etc., information about the speaker’s state and emotion can be revealed. This paper describes the dev

  1. Source Separation via Spectral Masking for Speech Recognition Systems

    Directory of Open Access Journals (Sweden)

    Gustavo Fernandes Rodrigues

    2012-12-01

    Full Text Available In this paper we present an insight into the use of spectral masking techniques in time-frequency domain, as a preprocessing step for the speech signal recognition. Speech recognition systems have their performance negatively affected in noisy environments or in the presence of other speech signals. The limits of these masking techniques for different levels of the signal-to-noise ratio are discussed. We show the robustness of the spectral masking techniques against four types of noise: white, pink, brown and human speech noise (bubble noise. The main contribution of this work is to analyze the performance limits of recognition systems  using spectral masking. We obtain an increase of 18% on the speech hit rate, when the speech signals were corrupted by other speech signals or bubble noise, with different signal-to-noise ratio of approximately 1, 10 and 20 dB. On the other hand, applying the ideal binary masks to mixtures corrupted by white, pink and brown noise, results an average growth of 9% on the speech hit rate, with the same different signal-to-noise ratio. The experimental results suggest that the masking spectral techniques are more suitable for the case when it is applied a bubble noise, which is produced by human speech, than for the case of applying white, pink and brown noise.

  2. Automatic TLI recognition system, programmer`s guide

    Energy Technology Data Exchange (ETDEWEB)

    Lassahn, G.D.

    1997-02-01

    This report describes the software of an automatic target recognition system (version 14), from a programmer`s point of view. The intent is to provide information that will help people who wish to modify the software. In separate volumes are a general description of the ATR system, Automatic TLI Recognition System, General Description, and a user`s manual, Automatic TLI Recognition System, User`s Guide. 2 refs.

  3. Speech-recognition interfaces for music information retrieval

    Science.gov (United States)

    Goto, Masataka

    2005-09-01

    This paper describes two hands-free music information retrieval (MIR) systems that enable a user to retrieve and play back a musical piece by saying its title or the artist's name. Although various interfaces for MIR have been proposed, speech-recognition interfaces suitable for retrieving musical pieces have not been studied. Our MIR-based jukebox systems employ two different speech-recognition interfaces for MIR, speech completion and speech spotter, which exploit intentionally controlled nonverbal speech information in original ways. The first is a music retrieval system with the speech-completion interface that is suitable for music stores and car-driving situations. When a user only remembers part of the name of a musical piece or an artist and utters only a remembered fragment, the system helps the user recall and enter the name by completing the fragment. The second is a background-music playback system with the speech-spotter interface that can enrich human-human conversation. When a user is talking to another person, the system allows the user to enter voice commands for music playback control by spotting a special voice-command utterance in face-to-face or telephone conversations. Experimental results from use of these systems have demonstrated the effectiveness of the speech-completion and speech-spotter interfaces. (Video clips: http://staff.aist.go.jp/m.goto/MIR/speech-if.html)

  4. Probabilistic SVM/GMM Classifier for Speaker-Independent Vowel Recognition in Continues Speech

    CERN Document Server

    Nazari, Mohammad; Valiollahzadeh, SeyedMajid

    2008-01-01

    In this paper, we discuss the issues in automatic recognition of vowels in Persian language. The present work focuses on new statistical method of recognition of vowels as a basic unit of syllables. First we describe a vowel detection system then briefly discuss how the detected vowels can feed to recognition unit. According to pattern recognition, Support Vector Machines (SVM) as a discriminative classifier and Gaussian mixture model (GMM) as a generative model classifier are two most popular techniques. Current state-ofthe- art systems try to combine them together for achieving more power of classification and improving the performance of the recognition systems. The main idea of the study is to combine probabilistic SVM and traditional GMM pattern classification with some characteristic of speech like band-pass energy to achieve better classification rate. This idea has been analytically formulated and tested on a FarsDat based vowel recognition system. The results show inconceivable increases in recogniti...

  5. Automatic transcription of continuous speech into syllable-like units for Indian languages

    Indian Academy of Sciences (India)

    G Lakshmi Sarada; A Lakshmi; Hema A Murthy; T Nagarajan

    2009-04-01

    The focus of this paper is to automatically segment and label continuous speech signal into syllable-like units for Indian languages. In this approach, the continuous speech signal is first automatically segmented into syllable-like units using group delay based algorithm. Similar syllable segments are then grouped together using an unsupervised and incremental training (UIT) technique. Isolated style HMM models are generated for each of the clusters during training. During testing, the speech signal is segmented into syllable-like units which are then tested against the HMMs obtained during training. This results in a syllable recognition performance of 42·6% and 39·94% for Tamil and Telugu. A new feature extraction technique that uses features extracted from multiple frame sizes and frame rates during both training and testing is explored for the syllable recognition task. This results in a recognition performance of 48·7% and 45·36%, for Tamil and Telugu respectively. The performance of segmentation followed by labelling is superior to that of a flat start syllable recogniser (27·8% and 28·8% for Tamil and Telugu respectively).

  6. Speech recognition for 40 patients receiving multichannel cochlear implants.

    Science.gov (United States)

    Dowell, R C; Mecklenburg, D J; Clark, G M

    1986-10-01

    We collected data on 40 patients who received the Nucleus multichannel cochlear implant. Results were reviewed to determine if the coding strategy is effective in transmitting the intended speech features and to assess patient benefit in terms of communication skills. All patients demonstrated significant improvement over preoperative results with a hearing aid for both lipreading enhancement and speech recognition without lipreading. Of the patients, 50% demonstrated ability to understand connected discourse with auditory input only. For the 23 patients who were tested 12 months postoperatively, there was substantial improvement in open-set speech recognition. PMID:3755975

  7. Effects of speech clarity on recognition memory for spoken sentences.

    Directory of Open Access Journals (Sweden)

    Kristin J Van Engen

    Full Text Available Extensive research shows that inter-talker variability (i.e., changing the talker affects recognition memory for speech signals. However, relatively little is known about the consequences of intra-talker variability (i.e. changes in speaking style within a talker on the encoding of speech signals in memory. It is well established that speakers can modulate the characteristics of their own speech and produce a listener-oriented, intelligibility-enhancing speaking style in response to communication demands (e.g., when speaking to listeners with hearing impairment or non-native speakers of the language. Here we conducted two experiments to examine the role of speaking style variation in spoken language processing. First, we examined the extent to which clear speech provided benefits in challenging listening environments (i.e. speech-in-noise. Second, we compared recognition memory for sentences produced in conversational and clear speaking styles. In both experiments, semantically normal and anomalous sentences were included to investigate the role of higher-level linguistic information in the processing of speaking style variability. The results show that acoustic-phonetic modifications implemented in listener-oriented speech lead to improved speech recognition in challenging listening conditions and, crucially, to a substantial enhancement in recognition memory for sentences.

  8. Histogram Equalization to Model Adaptation for Robust Speech Recognition

    Directory of Open Access Journals (Sweden)

    Hoirin Kim

    2010-01-01

    Full Text Available We propose a new model adaptation method based on the histogram equalization technique for providing robustness in noisy environments. The trained acoustic mean models of a speech recognizer are adapted into environmentally matched conditions by using the histogram equalization algorithm on a single utterance basis. For more robust speech recognition in the heavily noisy conditions, trained acoustic covariance models are efficiently adapted by the signal-to-noise ratio-dependent linear interpolation between trained covariance models and utterance-level sample covariance models. Speech recognition experiments on both the digit-based Aurora2 task and the large vocabulary-based task showed that the proposed model adaptation approach provides significant performance improvements compared to the baseline speech recognizer trained on the clean speech data.

  9. Mandarin Digits Speech Recognition Using Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    XIE Xiang; KUANG Jing-ming

    2005-01-01

    A method of applying support vector machine (SVM) in speech recognition was proposed, and a speech recognition system for mandarin digits was built up by SVMs. In the system, vectors were linearly extracted from speech feature sequence to make up time-aligned input patterns for SVM, and the decisions of several 2-class SVM classifiers were employed for constructing an N-class classifier. Four kinds of SVM kernel functions were compared in the experiments of speaker-independent speech recognition of mandarin digits. And the kernel of radial basis function has the highest accurate rate of 99.33%, which is better than that of the baseline system based on hidden Markov models (HMM) (97.08%). And the experiments also show that SVM can outperform HMM especially when the samples for learning were very limited.

  10. Cost-Efficient Development of Acoustic Models for Speech Recognition of Related Languages

    Directory of Open Access Journals (Sweden)

    J. Nouza

    2013-09-01

    Full Text Available When adapting an existing speech recognition system to a new language, major development costs are associated with the creation of an appropriate acoustic model (AM. For its training, a certain amount of recorded and annotated speech is required. In this paper, we show that not only the annotation process, but also the process of speech acquisition can be automated to minimize the need of human and expert work. We demonstrate the proposed methodology on Croatian language, for which the target AM has been built via cross-lingual adaptation of a Czech AM in 2 ways: a using commercially available GlobalPhone database, and b by automatic speech data mining from HRT radio archive. The latter approach is cost-free, yet it yields comparable or better results in LVCSR experiments conducted on 3 Croatian test sets.

  11. Post-Editing Error Correction Algorithm for Speech Recognition using Bing Spelling Suggestion

    CERN Document Server

    Bassil, Youssef

    2012-01-01

    ASR short for Automatic Speech Recognition is the process of converting a spoken speech into text that can be manipulated by a computer. Although ASR has several applications, it is still erroneous and imprecise especially if used in a harsh surrounding wherein the input speech is of low quality. This paper proposes a post-editing ASR error correction method and algorithm based on Bing's online spelling suggestion. In this approach, the ASR recognized output text is spell-checked using Bing's spelling suggestion technology to detect and correct misrecognized words. More specifically, the proposed algorithm breaks down the ASR output text into several word-tokens that are submitted as search queries to Bing search engine. A returned spelling suggestion implies that a query is misspelled; and thus it is replaced by the suggested correction; otherwise, no correction is performed and the algorithm continues with the next token until all tokens get validated. Experiments carried out on various speeches in differen...

  12. Lexicon Optimization for Dutch Speech Recognition in Spoken Document Retrieval

    NARCIS (Netherlands)

    Ordelman, Roeland; Hessen, van Arjan; Jong, de Franciska

    2001-01-01

    In this paper, ongoing work concerning the language modelling and lexicon optimization of a Dutch speech recognition system for Spoken Document Retrieval is described: the collection and normalization of a training data set and the optimization of our recognition lexicon. Effects on lexical coverage

  13. Lexicon optimization for Dutch speech recognition in spoken document retrieval

    NARCIS (Netherlands)

    Ordelman, Roeland; Hessen, van Arjan; Jong, de Franciska

    2001-01-01

    In this paper, ongoing work concerning the language modelling and lexicon optimization of a Dutch speech recognition system for Spoken Document Retrieval is described: the collection and normalization of a training data set and the optimization of our recognition lexicon. Effects on lexical coverage

  14. Towards automatic musical instrument timbre recognition

    Science.gov (United States)

    Park, Tae Hong

    This dissertation is comprised of two parts---focus on issues concerning research and development of an artificial system for automatic musical instrument timbre recognition and musical compositions. The technical part of the essay includes a detailed record of developed and implemented algorithms for feature extraction and pattern recognition. A review of existing literature introducing historical aspects surrounding timbre research, problems associated with a number of timbre definitions, and highlights of selected research activities that have had significant impact in this field are also included. The developed timbre recognition system follows a bottom-up, data-driven model that includes a pre-processing module, feature extraction module, and a RBF/EBF (Radial/Elliptical Basis Function) neural network-based pattern recognition module. 829 monophonic samples from 12 instruments have been chosen from the Peter Siedlaczek library (Best Service) and other samples from the Internet and personal collections. Significant emphasis has been put on feature extraction development and testing to achieve robust and consistent feature vectors that are eventually passed to the neural network module. In order to avoid a garbage-in-garbage-out (GIGO) trap and improve generality, extra care was taken in designing and testing the developed algorithms using various dynamics, different playing techniques, and a variety of pitches for each instrument with inclusion of attack and steady-state portions of a signal. Most of the research and development was conducted in Matlab. The compositional part of the essay includes brief introductions to "A d'Ess Are ," "Aboji," "48 13 N, 16 20 O," and "pH-SQ." A general outline pertaining to the ideas and concepts behind the architectural designs of the pieces including formal structures, time structures, orchestration methods, and pitch structures are also presented.

  15. Recognition of Emotions in Mexican Spanish Speech: An Approach Based on Acoustic Modelling of Emotion-Specific Vowels

    Directory of Open Access Journals (Sweden)

    Santiago-Omar Caballero-Morales

    2013-01-01

    Full Text Available An approach for the recognition of emotions in speech is presented. The target language is Mexican Spanish, and for this purpose a speech database was created. The approach consists in the phoneme acoustic modelling of emotion-specific vowels. For this, a standard phoneme-based Automatic Speech Recognition (ASR system was built with Hidden Markov Models (HMMs, where different phoneme HMMs were built for the consonants and emotion-specific vowels associated with four emotional states (anger, happiness, neutral, sadness. Then, estimation of the emotional state from a spoken sentence is performed by counting the number of emotion-specific vowels found in the ASR’s output for the sentence. With this approach, accuracy of 87–100% was achieved for the recognition of emotional state of Mexican Spanish speech.

  16. A Multi-Modal Recognition System Using Face and Speech

    Directory of Open Access Journals (Sweden)

    Samir Akrouf

    2011-05-01

    Full Text Available Nowadays Person Recognition has got more and more interest especially for security reasons. The recognition performed by a biometric system using a single modality tends to be less performing due to sensor data, restricted degrees of freedom and unacceptable error rates. To alleviate some of these problems we use multimodal biometric systems which provide better recognition results. By combining different modalities, such us speech, face, fingerprint, etc., we increase the performance of recognition systems. In this paper, we study the fusion of speech and face in a recognition system for taking a final decision (i.e., accept or reject identity claim. We evaluate the performance of each system differently then we fuse the results and compare the performances.

  17. Noise Robust Speech Recognition Applied to Voice-Driven Wheelchair

    Science.gov (United States)

    Sasou, Akira; Kojima, Hiroaki

    2009-12-01

    Conventional voice-driven wheelchairs usually employ headset microphones that are capable of achieving sufficient recognition accuracy, even in the presence of surrounding noise. However, such interfaces require users to wear sensors such as a headset microphone, which can be an impediment, especially for the hand disabled. Conversely, it is also well known that the speech recognition accuracy drastically degrades when the microphone is placed far from the user. In this paper, we develop a noise robust speech recognition system for a voice-driven wheelchair. This system can achieve almost the same recognition accuracy as the headset microphone without wearing sensors. We verified the effectiveness of our system in experiments in different environments, and confirmed that our system can achieve almost the same recognition accuracy as the headset microphone without wearing sensors.

  18. Comparison of HMM and DTW methods in automatic recognition of pathological phoneme pronunciation

    OpenAIRE

    Wielgat, Robert; Zielinski, Tomasz P.; Swietojanski, Pawel; Zoladz, Piotr; Król, Daniel; Wozniak, Tomasz; Grabias, Stanislaw

    2007-01-01

    In the paper recently proposed Human Factor Cepstral Coefficients (HFCC) are used to automatic recognition of pathological phoneme pronunciation in speech of impaired children and efficiency of this approach is compared to application of the standard Mel-Frequency Cepstral Coefficients (MFCC) as a feature vector. Both dynamic time warping (DTW), working on whole words or embedded phoneme patterns, and hidden Markov models (HMM) are used as classifiers in the presented research. Obtained resul...

  19. Multimodal Approach for Automatic Emotion Recognition Applied to the Tension Levels Study in TV Newscasts

    OpenAIRE

    Moisés Henrique Ramos Pereira; Flávio Luis Cardeal Pádua; Giani David Silva

    2015-01-01

    This article addresses a multimodal approach to automatic emotion recognition in participants of TV newscasts (presenters, reporters, commentators and others) able to assist the tension levels study in narratives of events in this television genre. The methodology applies state-of-the-art computational methods to process and analyze facial expressions, as well as speech signals. The proposed approach contributes to semiodiscoursive study of TV newscasts and their enunciative praxis, assisting...

  20. Speech recognition: Acoustic phonetic and lexical knowledge representation

    Science.gov (United States)

    Zue, V. W.

    1984-02-01

    The purpose of this program is to develop a speech data base facility under which the acoustic characteristics of speech sounds in various contexts can be studied conveniently; investigate the phonological properties of a large lexicon of, say 10,000 words and determine to what extent the phonotactic constraints can be utilized in speech recognition; study the acoustic cues that are used to mark work boundaries; develop a test bed in the form of a large-vocabulary, IWR system to study the interactions of acoustic, phonetic and lexical knowledge; and develop a limited continuous speech recognition system with the goal of recognizing any English word from its spelling in order to assess the interactions of higher-level knowledge sources.

  1. Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders

    CERN Document Server

    Baghai-Ravary, Ladan

    2013-01-01

    Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders provides a survey of methods designed to aid clinicians in the diagnosis and monitoring of speech disorders such as dysarthria and dyspraxia, with an emphasis on the signal processing techniques, statistical validity of the results presented in the literature, and the appropriateness of methods that do not require specialized equipment, rigorously controlled recording procedures or highly skilled personnel to interpret results. Such techniques offer the promise of a simple and cost-effective, yet objective, assessment of a range of medical conditions, which would be of great value to clinicians. The ideal scenario would begin with the collection of examples of the clients’ speech, either over the phone or using portable recording devices operated by non-specialist nursing staff. The recordings could then be analyzed initially to aid diagnosis of conditions, and subsequently to monitor the clients’ progress and res...

  2. Bayesian estimation of keyword confidence in Chinese continuous speech recognition

    Institute of Scientific and Technical Information of China (English)

    HAO Jie; LI Xing

    2003-01-01

    In a syllable-based speaker-independent Chinese continuous speech recognition system based on classical Hidden Markov Model (HMM), a Bayesian approach of keyword confidence estimation is studied, which utilizes both acoustic layer scores and syllable-based statistical language model (LM) score. The Maximum a posteriori (MAP) confidence measure is proposed, and the forward-backward algorithm calculating the MAP confidence scores is deduced. The performance of the MAP confidence measure is evaluated in keyword spotting application and the experiment results show that the MAP confidence scores provide high discriminability for keyword candidates. Furthermore, the MAP confidence measure can be applied to various speech recognition applications.

  3. Speech Recognition Method Based on Multilayer Chaotic Neural Network

    Institute of Scientific and Technical Information of China (English)

    REN Xiaolin; HU Guangrui

    2001-01-01

    In this paper,speech recognitionusing neural networks is investigated.Especially,chaotic dynamics is introduced to neurons,and a mul-tilayer chaotic neural network (MLCNN) architectureis built.A learning algorithm is also derived to trainthe weights of the network.We apply the MLCNNto speech recognition and compare the performanceof the network with those of recurrent neural net-work (RNN) and time-delay neural network (TDNN).Experimental results show that the MLCNN methodoutperforms the other neural networks methods withrespect to average recognition rate.

  4. Integration of Metamodel and Acoustic Model for Dysarthric Speech Recognition

    Directory of Open Access Journals (Sweden)

    Hironori Matsumasa

    2009-08-01

    Full Text Available We investigated the speech recognition of a person with articulation disorders resulting from athetoid cerebral palsy. The articulation of the first words spoken tends to be unstable due to the strain placed on the speech-related muscles, and this causes degradation of speech recognition. Therefore, we proposed a robust feature extraction method based on PCA (Principal Component Analysis instead of MFCC, where the main stable utterance element is projected onto low-order features and fluctuation elements of speech style are projected onto high-order features. Therefore, the PCA-based filter will be able to extract stable utterance features only. The fluctuation of speaking style may invoke phone fluctuations, such as substitutions, deletions and insertions. In this paper, we discuss our effort to integrate a Metamodel and an Acoustic model approach. Metamodels have a technique for incorporating a model of a speaker’s confusion matrix into the ASR process in such a way as to increase recognition accuracy. The integration of metamodels and acoustic models enables fluctuation suppression not only in feature extraction but also in recognition. The proposed method resulted in an improvement of 9.9% (from 79.1% to 89% in the recognition rate compared to the conventional method.

  5. Writing and Speech Recognition : Observing Error Correction Strategies of Professional Writers

    NARCIS (Netherlands)

    Leijten, M.A.J.C.

    2007-01-01

    In this thesis we describe the organization of speech recognition based writing processes. Writing can be seen as a visual representation of spoken language: a combination that speech recognition takes full advantage of. In the field of writing research, speech recognition is a new writing instrumen

  6. Improving user-friendliness by using visually supported speech recognition

    NARCIS (Netherlands)

    Waals, J.A.J.S.; Kooi, F.L.; Kriekaard, J.J.

    2002-01-01

    While speech recognition in principle may be one of the most natural interfaces, in practice it is not due to the lack of user-friendliness. Words are regularly interpreted wrong, and subjects tend to articulate in an exaggerated manner. We explored the potential of visually supported error correcti

  7. Speech emotion recognition based on statistical pitch model

    Institute of Scientific and Technical Information of China (English)

    WANG Zhiping; ZHAO Li; ZOU Cairong

    2006-01-01

    A modified Parzen-window method, which keep high resolution in low frequencies and keep smoothness in high frequencies, is proposed to obtain statistical model. Then, a gender classification method utilizing the statistical model is proposed, which have a 98% accuracy of gender classification while long sentence is dealt with. By separation the male voice and female voice, the mean and standard deviation of speech training samples with different emotion are used to create the corresponding emotion models. Then the Bhattacharyya distance between the test sample and statistical models of pitch, are utilized for emotion recognition in speech.The normalization of pitch for the male voice and female voice are also considered, in order to illustrate them into a uniform space. Finally, the speech emotion recognition experiment based on K Nearest Neighbor shows that, the correct rate of 81% is achieved, where it is only 73.85%if the traditional parameters are utilized.

  8. EMOTIONAL SPEECH RECOGNITION BASED ON SVM WITH GMM SUPERVECTOR

    Institute of Scientific and Technical Information of China (English)

    Chen Yanxiang; Xie Jian

    2012-01-01

    Emotion recognition from speech is an important field of research in human computer interaction.In this letter the framework of Support Vector Machines (SVM) with Gaussian Mixture Model (GMM) supervector is introduced for emotional speech recognition.Because of the importance of variance in reflecting the distribution of speech,the normalized mean vectors potential to exploit the information from the variance are adopted to form the GMM supervector.Comparative experiments from five aspects are conducted to study their corresponding effect to system performance.The experiment results,which indicate that the influence of number of mixtures is strong as well as influence of duration is weak,provide basis for the train set selection of Universal Background Model (UBM).

  9. POLISH EMOTIONAL SPEECH RECOGNITION USING ARTIFICAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Paweł Powroźnik

    2014-11-01

    Full Text Available The article presents the issue of emotion recognition based on polish emotional speech analysis. The Polish database of emotional speech, prepared and shared by the Medical Electronics Division of the Lodz University of Technology, has been used for research. The following parameters extracted from sampled and normalised speech signal has been used for the analysis: energy of signal, speaker’s sex, average value of speech signal and both the minimum and maximum sample value for a given signal. As an emotional state a classifier fof our layers of artificial neural network has been used. The achieved results reach 50% of accuracy. Conducted researches focused on six emotional states: a neutral state, sadness, joy, anger, fear and boredom.

  10. Speed and automaticity of word recognition - inseparable twins?

    DEFF Research Database (Denmark)

    Poulsen, Mads; Asmussen, Vibeke; Elbro, Carsten

    'Speed and automaticity' of word recognition is a standard collocation. However, it is not clear whether speed and automaticity (i.e., effortlessness) make independent contributions to reading comprehension. In theory, both speed and automaticity may save cognitive resources for comprehension...... processes. Hence, the aim of the present study was to assess the unique contributions of word recognition speed and automaticity to reading comprehension while controlling for decoding speed and accuracy. Method: 139 Grade 5 students completed tests of reading comprehension and computer-based tests of speed...... developmental sources. However, multiple regression analyses indicated that both automaticity (effortlessness) and speed of word recognition (word-specific orthographic knowledge) contributed unique variance to reading comprehension when word decoding accuracy and speed was controlled. Conclusion: The results...

  11. Environment-dependent denoising autoencoder for distant-talking speech recognition

    Science.gov (United States)

    Ueda, Yuma; Wang, Longbiao; Kai, Atsuhiko; Ren, Bo

    2015-12-01

    In this paper, we propose an environment-dependent denoising autoencoder (DAE) and automatic environment identification based on a deep neural network (DNN) with blind reverberation estimation for robust distant-talking speech recognition. Recently, DAEs have been shown to be effective in many noise reduction and reverberation suppression applications because higher-level representations and increased flexibility of the feature mapping function can be learned. However, a DAE is not adequate in mismatched training and test environments. In a conventional DAE, parameters are trained using pairs of reverberant speech and clean speech under various acoustic conditions (that is, an environment-independent DAE). To address the above problem, we propose two environment-dependent DAEs to reduce the influence of mismatches between training and test environments. In the first approach, we train various DAEs using speech from different acoustic environments, and the DAE for the condition that best matches the test condition is automatically selected (that is, a two-step environment-dependent DAE). To improve environment identification performance, we propose a DNN that uses both reverberant speech and estimated reverberation. In the second approach, we add estimated reverberation features to the input of the DAE (that is, a one-step environment-dependent DAE or a reverberation-aware DAE). The proposed method is evaluated using speech in simulated and real reverberant environments. Experimental results show that the environment-dependent DAE outperforms the environment-independent one in both simulated and real reverberant environments. For two-step environment-dependent DAE, the performance of environment identification based on the proposed DNN approach is also better than that of the conventional DNN approach, in which only reverberant speech is used and reverberation is not blindly estimated. And, the one-step environment-dependent DAE significantly outperforms the two

  12. Adaptive Recognition of Phonemes from Speaker - Connected-Speech Using Alisa.

    Science.gov (United States)

    Osella, Stephen Albert

    The purpose of this dissertation research is to investigate a novel approach to automatic speech recognition (ASR). The successes that have been achieved in ASR have relied heavily on the use of a language grammar, which significantly constrains the ASR process. By using grammar to provide most of the recognition ability, the ASR system does not have to be as accurate at the low-level recognition stage. The ALISA Phonetic Transcriber (APT) algorithm is proposed as a way to improve ASR by enhancing the lowest -level recognition stage. The objective of the APT algorithm is to classify speech frames (a short sequence of speech signal samples) into a small set of phoneme classes. The APT algorithm constructs the mapping from speech frames to phoneme labels through a multi-layer feedforward process. A design principle of APT is that final decisions are delayed as long as possible. Instead of attempting to optimize the decision making at each processing level individually, each level generates a list of candidate solutions that are passed on to the next level of processing. The later processing levels use these candidate solutions to resolve ambiguities. The scope of this dissertation is the design of the APT algorithm up to the speech-frame classification stage. In future research, the APT algorithm will be extended to the word recognition stage. In particular, the APT algorithm could serve as the front-end stage to a Hidden Markov Model (HMM) based word recognition system. In such a configuration, the APT algorithm would provide the HMM with the requisite phoneme state-probability estimates. To date, the APT algorithm has been tested with the TIMIT and NTIMIT speech databases. The APT algorithm has been trained and tested on the SX and SI sentence texts using both male and female speakers. Results indicate better performance than those results obtained using a neural network based speech-frame classifier. The performance of the APT algorithm has been evaluated for

  13. New Ideas for Speech Recognition and Related Technologies

    Energy Technology Data Exchange (ETDEWEB)

    Holzrichter, J F

    2002-06-17

    The ideas relating to the use of organ motion sensors for the purposes of speech recognition were first described by.the author in spring 1994. During the past year, a series of productive collaborations between the author, Tom McEwan and Larry Ng ensued and have lead to demonstrations, new sensor ideas, and algorithmic descriptions of a large number of speech recognition concepts. This document summarizes the basic concepts of recognizing speech once organ motions have been obtained. Micro power radars and their uses for the measurement of body organ motions, such as those of the heart and lungs, have been demonstrated by Tom McEwan over the past two years. McEwan and I conducted a series of experiments, using these instruments, on vocal organ motions beginning in late spring, during which we observed motions of vocal folds (i.e., cords), tongue, jaw, and related organs that are very useful for speech recognition and other purposes. These will be reviewed in a separate paper. Since late summer 1994, Lawrence Ng and I have worked to make many of the initial recognition ideas more rigorous and to investigate the applications of these new ideas to new speech recognition algorithms, to speech coding, and to speech synthesis. I introduce some of those ideas in section IV of this document, and we describe them more completely in the document following this one, UCRL-UR-120311. For the design and operation of micro-power radars and their application to body organ motions, the reader may contact Tom McEwan directly. The capability for using EM sensors (i.e., radar units) to measure body organ motions and positions has been available for decades. Impediments to their use appear to have been size, excessive power, lack of resolution, and lack of understanding of the value of organ motion measurements, especially as applied to speech related technologies. However, with the invention of very low power, portable systems as demonstrated by McEwan at LLNL researchers have begun

  14. Temporal visual cues aid speech recognition

    DEFF Research Database (Denmark)

    Zhou, Xiang; Ross, Lars; Lehn-Schiøler, Tue;

    2006-01-01

    BACKGROUND: It is well known that under noisy conditions, viewing a speaker's articulatory movement aids the recognition of spoken words. Conventionally it is thought that the visual input disambiguates otherwise confusing auditory input. HYPOTHESIS: In contrast we hypothesize that it is the temp......BACKGROUND: It is well known that under noisy conditions, viewing a speaker's articulatory movement aids the recognition of spoken words. Conventionally it is thought that the visual input disambiguates otherwise confusing auditory input. HYPOTHESIS: In contrast we hypothesize...... that it is the temporal synchronicity of the visual input that aids parsing of the auditory stream. More specifically, we expected that purely temporal information, which does not convey information such as place of articulation may facility word recognition. METHODS: To test this prediction we used temporal features...... of audio to generate an artificial talking-face video and measured word recognition performance on simple monosyllabic words. RESULTS: When presenting words together with the artificial video we find that word recognition is improved over purely auditory presentation. The effect is significant (p...

  15. Automatic sign language recognition inspired by human sign perception

    NARCIS (Netherlands)

    Ten Holt, G.A.

    2010-01-01

    Automatic sign language recognition is a relatively new field of research (since ca. 1990). Its objectives are to automatically analyze sign language utterances. There are several issues within the research area that merit investigation: how to capture the utterances (cameras, magnetic sensors, inst

  16. Statistical pattern recognition for automatic writer identification and verification

    NARCIS (Netherlands)

    Bulacu, Marius Lucian

    2007-01-01

    The thesis addresses the problem of automatic person identification using scanned images of handwriting.Identifying the author of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with direct applicability in the forensic and historic document ana

  17. Biologically inspired emotion recognition from speech

    Directory of Open Access Journals (Sweden)

    Buscicchio Cosimo

    2011-01-01

    Full Text Available Abstract Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.

  18. Biologically inspired emotion recognition from speech

    Science.gov (United States)

    Caponetti, Laura; Buscicchio, Cosimo Alessandro; Castellano, Giovanna

    2011-12-01

    Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM) recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC) and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.

  19. Dynamic Relation Between Working Memory Capacity and Speech Recognition in Noise During the First 6 Months of Hearing Aid Use

    Directory of Open Access Journals (Sweden)

    Elaine H. N. Ng

    2014-11-01

    Full Text Available The present study aimed to investigate the changing relationship between aided speech recognition and cognitive function during the first 6 months of hearing aid use. Twenty-seven first-time hearing aid users with symmetrical mild to moderate sensorineural hearing loss were recruited. Aided speech recognition thresholds in noise were obtained in the hearing aid fitting session as well as at 3 and 6 months postfitting. Cognitive abilities were assessed using a reading span test, which is a measure of working memory capacity, and a cognitive test battery. Results showed a significant correlation between reading span and speech reception threshold during the hearing aid fitting session. This relation was significantly weakened over the first 6 months of hearing aid use. Multiple regression analysis showed that reading span was the main predictor of speech recognition thresholds in noise when hearing aids were first fitted, but that the pure-tone average hearing threshold was the main predictor 6 months later. One way of explaining the results is that working memory capacity plays a more important role in speech recognition in noise initially rather than after 6 months of use. We propose that new hearing aid users engage working memory capacity to recognize unfamiliar processed speech signals because the phonological form of these signals cannot be automatically matched to phonological representations in long-term memory. As familiarization proceeds, the mismatch effect is alleviated, and the engagement of working memory capacity is reduced.

  20. Text Independent Speaker Recognition and Speaker Independent Speech Recognition Using Iterative Clustering Approach

    Directory of Open Access Journals (Sweden)

    A.Revathi

    2009-11-01

    Full Text Available This paper presents the effectiveness of perceptual features and iterative clustering approach forperforming both speech and speaker recognition. Procedure used for formation of training speech is differentfor developing training models for speaker independent speech and text independent speaker recognition. So,this work mainly emphasizes the utilization of clustering models developed for the training data to obtainbetter accuracy as 91%, 91% and 99.5% for mel frequency perceptual linear predictive cepstrum with respectto three categories such as speaker identification, isolated digit recognition and continuous speechrecognition. This feature also produces 9% as low equal error rate which is used as a performance measurefor speaker verification. The work is experimentally evaluated on the set of isolated digits and continuousspeeches from TI digits_1 and TI digits_2 database for speech recognition and on speeches of 50 speakersrandomly chosen from TIMIT database for speaker recognition. The noteworthy feature of speakerrecognition algorithm is to evaluate the testing procedure on identical messages of all the 50 speakers,theoretical validation of results using F-ratio and validation of results by statistical analysis using2 cdistribution.

  1. Sign language perception research for improving automatic sign language recognition

    NARCIS (Netherlands)

    Ten Holt, G.A.; Arendsen, J.; De Ridder, H.; Van Doorn, A.J.; Reinders, M.J.T.; Hendriks, E.A.

    2009-01-01

    Current automatic sign language recognition (ASLR) seldom uses perceptual knowledge about the recognition of sign language. Using such knowledge can improve ASLR because it can give an indication which elements or phases of a sign are important for its meaning. Also, the current generation of data-d

  2. Automatic Speech Segmentation Based On Audio and Optical Flow Visual Classification

    Directory of Open Access Journals (Sweden)

    Behnam Torabi

    2014-10-01

    Full Text Available Automatic speech segmentation as an important part of speech recognition system (ASR is highly noise dependent. Noise is made by changes in the communication channel, background, level of speaking etc. In recent years, many researchers have proposed noise cancelation techniques and have added visual features from speaker’s face to reduce the effect of noise on ASR systems. Removing noise from audio signals depends on the type of the noise; so it cannot be used as a general solution. Adding visual features improve this lack of efficiency, but advanced methods of this type need manual extraction of visual features. In this paper we propose a completely automatic system which uses optical flow vectors from speaker’s image sequence to obtain visual features. Then, Hidden Markov Models are trained to segment audio signals from image sequences and audio features based on extracted optical flow. The developed segmentation system based on such method acts totally automatic and become more robust to noise.

  3. Automatic local Gabor Features extraction for face recognition

    CERN Document Server

    Jemaa, Yousra Ben

    2009-01-01

    We present in this paper a biometric system of face detection and recognition in color images. The face detection technique is based on skin color information and fuzzy classification. A new algorithm is proposed in order to detect automatically face features (eyes, mouth and nose) and extract their correspondent geometrical points. These fiducial points are described by sets of wavelet components which are used for recognition. To achieve the face recognition, we use neural networks and we study its performances for different inputs. We compare the two types of features used for recognition: geometric distances and Gabor coefficients which can be used either independently or jointly. This comparison shows that Gabor coefficients are more powerful than geometric distances. We show with experimental results how the importance recognition ratio makes our system an effective tool for automatic face detection and recognition.

  4. Voice Activity Detector of Wake-Up-Word Speech Recognition System Design on FPGA

    OpenAIRE

    Veton Z. Këpuska; Mohamed M. Eljhani; Brian H. Hight

    2014-01-01

    A typical speech recognition system is push-to-talk operated that requires activation. However for those who use hands-busy applications, movement may by restricted or impossible. One alternative is to use Speech-Only Interface. The proposed method that is called Wake-Up-Word Speech Recognition (WUW-SR) that utilizes speech only interface. A WUW-SR system would allow the user to activate systems (Cell phone, Computer, etc.) with only speech commands instead of manual activation. T...

  5. EMOTION RECOGNITION FROM SPEECH SIGNAL: REALIZATION AND AVAILABLE TECHNIQUES

    Directory of Open Access Journals (Sweden)

    NILIM JYOTI GOGOI

    2014-05-01

    Full Text Available The ability to detect human emotion from their speech is going to be a great addition in the field of human-robot interaction. The aim of the work is to build an emotion recognition system using Mel-frequency cepstral coefficients (MFCC and Gaussian mixture model (GMM classifier. Basically the purpose of the work is aimed at describing the best possible and available methods for recognizing emotion from an emotional speech. For that reason already existing techniques and used methods for feature extraction and pattern classification have been reviewed and discussed in this paper.

  6. Speech Recognition Using HMM with MFCC-An Analysis Using Frequency Specral Decomposion Technique

    Directory of Open Access Journals (Sweden)

    Ibrahim Patel

    2010-12-01

    Full Text Available This paper presents an approach to the recognition of speech signal using frequency spectral information with Mel frequency for the improvement of speech feature representation in a HMM based recognition approach. A frequency spectral information is incorporated to the conventional Mel spectrum base speech recognition approach. The Mel frequency approach exploits the frequency observation for speech signal in a given resolution which results in resolution feature overlapping resulting in recognition limit. Resolution decomposition with separating frequency is mapping approach for a HMM based speech recognition system. The Simulation results show an improvement in the quality metrics of speech recognition with respect to computational time, learning accuracy for a speech recognition system.

  7. Noise robust speech recognition with support vector learning algorithms

    Science.gov (United States)

    Namarvar, Hassan H.; Berger, Theodore W.

    2001-05-01

    We propose a new noise robust speech recognition system using time-frequency domain analysis and radial basis function (RBF) support vector machines (SVM). Here, we ignore the effects of correlative and nonstationary noise and only focus on continuous additive Gaussian white noise. We then develop an isolated digit/command recognizer and compare its performance to two other systems, in which the SVM classifier has been replaced by multilayer perceptron (MLP) and RBF neural networks. All systems are trained under the low signal-to-noise ratio (SNR) condition. We obtained the best correct classification rate of 83% and 52% for digit recognition on the TI-46 corpus for the SVM and MLP systems, respectively under the SNR=0 (dB), while we could not train the RBF network for the same dataset. The newly developed speech recognition system seems to be noise robust for medium size speech recognition problems under continuous, stationary background noise. However, it is still required to test the system under realistic noisy environment to observe whether the system keeps its adaptability and robustness under such conditions. [Work supported in part by grants from DARPA CBS, NASA, and ONR.

  8. Drawing Recognition for Automatic Dimensioning of Shear-Walls

    Institute of Scientific and Technical Information of China (English)

    任爱珠; 喻强; 许云

    2002-01-01

    In computer-aided structural design, the drawing of shear-walls cannot be easily automated; however, dimensioning of the shear-walls provides a method to automate the drawing. This paper presents a drawing recognition method for automatic dimensioning of shear-walls. The regional relationship method includes a graphic shape template library that can learn new shear-wall shapes. The automatic dimensioning of shear-walls is then realized by matching the templates. The regional relationship method for graph recognition effectively describes the topological relationships for graphs to significantly increase the recognition efficiency.

  9. Multiobjective image recognition algorithm in the fully automatic die bonder

    Institute of Scientific and Technical Information of China (English)

    JIANG Kai; CHEN Hai-xia; YUAN Sen-miao

    2006-01-01

    It is a very important task to automatically fix the number of die in the image recognition system of a fully automatic die bonder.A multiobjective image recognition algorithm based on clustering Genetic Algorithm (GA),is proposed in this paper.In the evolutionary process of GA,a clustering method is provided that utilizes information from the template and the fitness landscape of the current population..The whole population is grouped into different niches by the clustering method.Experimental results demonstrated that the number of target images could be determined by the algorithm automatically,and multiple targets could be recognized at a time.As a result,time consumed by one image recognition is shortened,the performance of the image recognition system is improved,and the atomization of the system is fulfilled.

  10. Using vector Taylor series with noise clustering for speech recognition in non-stationary noisy environments

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The performance of automatic speech recognizer degrades seriously when there are mismatches between the training and testing conditions. Vector Taylor Series (VTS) approach has been used to compensate mismatches caused by additive noise and convolutive channel distortion in the cepstral domain. In this paper, the conventional VTS is extended by incorporating noise clustering into its EM iteration procedure, improving its compensation effectiveness under non-stationary noisy environments. Recognition experiments under babble and exhibition noisy environments demonstrate that the new algorithm achieves35 % average error rate reduction compared with the conventional VTS.

  11. Robust Speaker Recognition with Combined Use of Acoustic and Throat Microphone Speech

    DEFF Research Database (Denmark)

    Sahidullah, Md; Gonzalez Hautamäki, Rosa; Thomsen, Dennis Alexander Lehmann;

    2016-01-01

    Accuracy of automatic speaker recognition (ASV) systems degrades severely in the presence of background noise. In this paper, we study the use of additional side information provided by a body-conducted sensor, throat microphone. Throat microphone signal is much less affected by background noise...... of this additional information for both speech activity detection, feature extraction and fusion of the acoustic and throat microphone signals. We collect a pilot database consisting of 38 subjects including both clean and noisy sessions. We carry out speaker verification experiments using Gaussian mixture model...

  12. Automatic, Dimensional and Continuous Emotion Recognition

    NARCIS (Netherlands)

    Gunes, Hatice; Pantic, Maja; Vallverdú, J.

    2010-01-01

    Recognition and analysis of human emotions have attracted a lot of interest in the past two decades and have been researched extensively in neuroscience, psychology, cognitive sciences, and computer sciences. Most of the past research in machine analysis of human emotion has focused on recognition o

  13. An overview of the SPHINX speech recognition system

    Science.gov (United States)

    Lee, Kai-Fu; Hon, Hsiao-Wuen; Reddy, Raj

    1990-01-01

    A description is given of SPHINX, a system that demonstrates the feasibility of accurate, large-vocabulary, speaker-independent, continuous speech recognition. SPHINX is based on discrete hidden Markov models (HMMs) with linear-predictive-coding derived parameters. To provide speaker independence, knowledge was added to these HMMs in several ways: multiple codebooks of fixed-width parameters, and an enhanced recognizer with carefully designed models and word-duration modeling. To deal with coarticulation in continuous speech, yet still adequately represent a large vocabulary, two new subword speech units are introduced: function-word-dependent phone models and generalized triphone models. With grammars of perplexity 997, 60, and 20, SPHINX attained word accuracies of 71, 94, and 96 percent, respectively, on a 997-word task.

  14. Automatic Facial Expression Recognition Based on Hybrid Approach

    Directory of Open Access Journals (Sweden)

    Ali K. K. Bermani

    2012-12-01

    Full Text Available The topic of automatic recognition of facial expressions deduce a lot of researchers in the late last century and has increased a great interest in the past few years. Several techniques have emerged in order to improve the efficiency of the recognition by addressing problems in face detection and extraction features in recognizing expressions. This paper has proposed automatic system for facial expression recognition which consists of hybrid approach in feature extraction phase which represent a combination between holistic and analytic approaches by extract 307 facial expression features (19 features by geometric, 288 feature by appearance. Expressions recognition is performed by using radial basis function (RBF based on artificial neural network to recognize the six basic emotions (anger, fear, disgust, happiness, surprise, sadness in addition to the natural.The system achieved recognition rate 97.08% when applying on person-dependent database and 93.98% when applying on person-independent.

  15. Initial evaluation of a continuous speech recognition program for radiology

    OpenAIRE

    Kanal, KM; Hangiandreou, NJ; Sykes, AM; Eklund, HE; Araoz, PA; Leon, JA; Erickson, BJ

    2001-01-01

    The aims of this work were to measure the accuracy of one continuous speech recognition product and dependence on the speaker's gender and status as a native or nonnative English speaker, and evaluate the product's potential for routine use in transcribing radiology reports. IBM MedSpeak/Radiology software, version 1.1 was evaluated by 6 speakers. Two were nonnative English speakers, and 3 were men. Each speaker dictated a set of 12 reports. The reports included neurologic and body imaging ex...

  16. An audio-visual corpus for multimodal speech recognition in Dutch language

    NARCIS (Netherlands)

    Wojdel, J.; Wiggers, P.; Rothkrantz, L.J.M.

    2002-01-01

    This paper describes the gathering and availability of an audio-visual speech corpus for Dutch language. The corpus was prepared with the multi-modal speech recognition in mind and it is currently used in our research on lip-reading and bimodal speech recognition. It contains the prompts used also i

  17. Syntactic error modeling and scoring normalization in speech recognition: Error modeling and scoring normalization in the speech recognition task for adult literacy training

    Science.gov (United States)

    Olorenshaw, Lex; Trawick, David

    1991-01-01

    The purpose was to develop a speech recognition system to be able to detect speech which is pronounced incorrectly, given that the text of the spoken speech is known to the recognizer. Better mechanisms are provided for using speech recognition in a literacy tutor application. Using a combination of scoring normalization techniques and cheater-mode decoding, a reasonable acceptance/rejection threshold was provided. In continuous speech, the system was tested to be able to provide above 80 pct. correct acceptance of words, while correctly rejecting over 80 pct. of incorrectly pronounced words.

  18. Exploiting temporal correlation of speech for error robust and bandwidth flexible distributed speech recognition

    DEFF Research Database (Denmark)

    Tan, Zheng-Hua; Dalsgaard, Paul; Lindberg, Børge

    2007-01-01

    In this paper the temporal correlation of speech is exploited in front-end feature extraction, client based error recovery and server based error concealment (EC) for distributed speech recognition. First, the paper investigates a half frame rate (HFR) front-end that uses double frame shifting...... features creates a set of error recovery techniques encompassing multiple description coding and interleaving schemes where interleaving has the advantage of not introducing a delay when there are no transmission errors. Thirdly, a sub-vector based EC technique is presented where error detection...

  19. Age-Related Differences in Lexical Access Relate to Speech Recognition in Noise.

    Science.gov (United States)

    Carroll, Rebecca; Warzybok, Anna; Kollmeier, Birger; Ruigendijk, Esther

    2016-01-01

    Vocabulary size has been suggested as a useful measure of "verbal abilities" that correlates with speech recognition scores. Knowing more words is linked to better speech recognition. How vocabulary knowledge translates to general speech recognition mechanisms, how these mechanisms relate to offline speech recognition scores, and how they may be modulated by acoustical distortion or age, is less clear. Age-related differences in linguistic measures may predict age-related differences in speech recognition in noise performance. We hypothesized that speech recognition performance can be predicted by the efficiency of lexical access, which refers to the speed with which a given word can be searched and accessed relative to the size of the mental lexicon. We tested speech recognition in a clinical German sentence-in-noise test at two signal-to-noise ratios (SNRs), in 22 younger (18-35 years) and 22 older (60-78 years) listeners with normal hearing. We also assessed receptive vocabulary, lexical access time, verbal working memory, and hearing thresholds as measures of individual differences. Age group, SNR level, vocabulary size, and lexical access time were significant predictors of individual speech recognition scores, but working memory and hearing threshold were not. Interestingly, longer accessing times were correlated with better speech recognition scores. Hierarchical regression models for each subset of age group and SNR showed very similar patterns: the combination of vocabulary size and lexical access time contributed most to speech recognition performance; only for the younger group at the better SNR (yielding about 85% correct speech recognition) did vocabulary size alone predict performance. Our data suggest that successful speech recognition in noise is mainly modulated by the efficiency of lexical access. This suggests that older adults' poorer performance in the speech recognition task may have arisen from reduced efficiency in lexical access; with an

  20. Age-Related Differences in Lexical Access Relate to Speech Recognition in Noise

    Science.gov (United States)

    Carroll, Rebecca; Warzybok, Anna; Kollmeier, Birger; Ruigendijk, Esther

    2016-01-01

    Vocabulary size has been suggested as a useful measure of “verbal abilities” that correlates with speech recognition scores. Knowing more words is linked to better speech recognition. How vocabulary knowledge translates to general speech recognition mechanisms, how these mechanisms relate to offline speech recognition scores, and how they may be modulated by acoustical distortion or age, is less clear. Age-related differences in linguistic measures may predict age-related differences in speech recognition in noise performance. We hypothesized that speech recognition performance can be predicted by the efficiency of lexical access, which refers to the speed with which a given word can be searched and accessed relative to the size of the mental lexicon. We tested speech recognition in a clinical German sentence-in-noise test at two signal-to-noise ratios (SNRs), in 22 younger (18–35 years) and 22 older (60–78 years) listeners with normal hearing. We also assessed receptive vocabulary, lexical access time, verbal working memory, and hearing thresholds as measures of individual differences. Age group, SNR level, vocabulary size, and lexical access time were significant predictors of individual speech recognition scores, but working memory and hearing threshold were not. Interestingly, longer accessing times were correlated with better speech recognition scores. Hierarchical regression models for each subset of age group and SNR showed very similar patterns: the combination of vocabulary size and lexical access time contributed most to speech recognition performance; only for the younger group at the better SNR (yielding about 85% correct speech recognition) did vocabulary size alone predict performance. Our data suggest that successful speech recognition in noise is mainly modulated by the efficiency of lexical access. This suggests that older adults’ poorer performance in the speech recognition task may have arisen from reduced efficiency in lexical access

  1. Error analysis to improve the speech recognition accuracy on Telugu language

    Indian Academy of Sciences (India)

    N Usha Rani; P N Girija

    2012-12-01

    Speech is one of the most important communication channels among the people. Speech Recognition occupies a prominent place in communication between the humans and machine. Several factors affect the accuracy of the speech recognition system. Much effort was involved to increase the accuracy of the speech recognition system, still erroneous output is generating in current speech recognition systems. Telugu language is one of the most widely spoken south Indian languages. In the proposed Telugu speech recognition system, errors obtained from decoder are analysed to improve the performance of the speech recognition system. Static pronunciation dictionary plays a key role in the speech recognition accuracy. Modification should be performed in the dictionary, which is used in the decoder of the speech recognition system. This modification reduces the number of the confusion pairs which improves the performance of the speech recognition system. Language model scores are also varied with this modification. Hit rate is considerably increased during this modification and false alarms have been changing during the modification of the pronunciation dictionary. Variations are observed in different error measures such as F-measures, error-rate and Word Error Rate (WER) by application of the proposed method.

  2. A study of speech emotion recognition based on hybrid algorithm

    Science.gov (United States)

    Zhu, Ju-xia; Zhang, Chao; Lv, Zhao; Rao, Yao-quan; Wu, Xiao-pei

    2011-10-01

    To effectively improve the recognition accuracy of the speech emotion recognition system, a hybrid algorithm which combines Continuous Hidden Markov Model (CHMM), All-Class-in-One Neural Network (ACON) and Support Vector Machine (SVM) is proposed. In SVM and ACON methods, some global statistics are used as emotional features, while in CHMM method, instantaneous features are employed. The recognition rate by the proposed method is 92.25%, with the rejection rate to be 0.78%. Furthermore, it obtains the relative increasing of 8.53%, 4.69% and 0.78% compared with ACON, CHMM and SVM methods respectively. The experiment result confirms the efficiency of distinguishing anger, happiness, neutral and sadness emotional states.

  3. Sign language perception research for improving automatic sign language recognition

    OpenAIRE

    Ten Holt, G.A.; Arendsen, J.; De Ridder, H.; Van Doorn, A.J.; Reinders, M.J.T.; Hendriks, E.A.

    2009-01-01

    Current automatic sign language recognition (ASLR) seldom uses perceptual knowledge about the recognition of sign language. Using such knowledge can improve ASLR because it can give an indication which elements or phases of a sign are important for its meaning. Also, the current generation of data-driven ASLR methods has shortcomings which may not be solvable without the use of knowledge on human sign language processing. Handling variation in the precise execution of signs is an example of s...

  4. Improving on hidden Markov models: An articulatorily constrained, maximum likelihood approach to speech recognition and speech coding

    Energy Technology Data Exchange (ETDEWEB)

    Hogden, J.

    1996-11-05

    The goal of the proposed research is to test a statistical model of speech recognition that incorporates the knowledge that speech is produced by relatively slow motions of the tongue, lips, and other speech articulators. This model is called Maximum Likelihood Continuity Mapping (Malcom). Many speech researchers believe that by using constraints imposed by articulator motions, we can improve or replace the current hidden Markov model based speech recognition algorithms. Unfortunately, previous efforts to incorporate information about articulation into speech recognition algorithms have suffered because (1) slight inaccuracies in our knowledge or the formulation of our knowledge about articulation may decrease recognition performance, (2) small changes in the assumptions underlying models of speech production can lead to large changes in the speech derived from the models, and (3) collecting measurements of human articulator positions in sufficient quantity for training a speech recognition algorithm is still impractical. The most interesting (and in fact, unique) quality of Malcom is that, even though Malcom makes use of a mapping between acoustics and articulation, Malcom can be trained to recognize speech using only acoustic data. By learning the mapping between acoustics and articulation using only acoustic data, Malcom avoids the difficulties involved in collecting articulator position measurements and does not require an articulatory synthesizer model to estimate the mapping between vocal tract shapes and speech acoustics. Preliminary experiments that demonstrate that Malcom can learn the mapping between acoustics and articulation are discussed. Potential applications of Malcom aside from speech recognition are also discussed. Finally, specific deliverables resulting from the proposed research are described.

  5. Multilevel Analysis in Analyzing Speech Data

    Science.gov (United States)

    Guddattu, Vasudeva; Krishna, Y.

    2011-01-01

    The speech produced by human vocal tract is a complex acoustic signal, with diverse applications in phonetics, speech synthesis, automatic speech recognition, speaker identification, communication aids, speech pathology, speech perception, machine translation, hearing research, rehabilitation and assessment of communication disorders and many…

  6. Speaker-Adaptive Speech Recognition Based on Surface Electromyography

    Science.gov (United States)

    Wand, Michael; Schultz, Tanja

    We present our recent advances in silent speech interfaces using electromyographic signals that capture the movements of the human articulatory muscles at the skin surface for recognizing continuously spoken speech. Previous systems were limited to speaker- and session-dependent recognition tasks on small amounts of training and test data. In this article we present speaker-independent and speaker-adaptive training methods which allow us to use a large corpus of data from many speakers to train acoustic models more reliably. We use the speaker-dependent system as baseline, carefully tuning the data preprocessing and acoustic modeling. Then on our corpus we compare the performance of speaker-dependent and speaker-independent acoustic models and carry out model adaptation experiments.

  7. Two Systems for Automatic Music Genre Recognition

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    2012-01-01

    We re-implement and test two state-of-the-art systems for automatic music genre classification; but unlike past works in this area, we look closer than ever before at their behavior. First, we look at specific instances where each system consistently applies the same wrong label across multiple...... trials of cross-validation. Second, we test the robustness of each system to spectral equalization. Finally, we test how well human subjects recognize the genres of music excerpts composed by each system to be highly genre representative. Our results suggest that neither high-performing system has...... a capacity to recognize music genre....

  8. Radar automatic target recognition (ATR) and non-cooperative target recognition (NCTR)

    CERN Document Server

    Blacknell, David

    2013-01-01

    The ability to detect and locate targets by day or night, over wide areas, regardless of weather conditions has long made radar a key sensor in many military and civil applications. However, the ability to automatically and reliably distinguish different targets represents a difficult challenge. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) captures material presented in the NATO SET-172 lecture series to provide an overview of the state-of-the-art and continuing challenges of radar target recognition. Topics covered include the problem as applied to th

  9. Quality Assessment of Compressed Video for Automatic License Plate Recognition

    DEFF Research Database (Denmark)

    Ukhanova, Ann; Støttrup-Andersen, Jesper; Forchhammer, Søren;

    2014-01-01

    Definition of video quality requirements for video surveillance poses new questions in the area of quality assessment. This paper presents a quality assessment experiment for an automatic license plate recognition scenario. We explore the influence of the compression by H.264/AVC and H.265/HEVC...

  10. Laser gated viewing : An enabler for Automatic Target Recognition?

    NARCIS (Netherlands)

    Bovenkamp, E.G.P.; Schutte, K.

    2010-01-01

    For many decades attempts to accomplish Automatic Target Recognition have been made using both visual and FLIR camera systems. A recurring problem in these approaches is the segmentation problem, which is the separation between the target and its background. This paper describes an approach to Autom

  11. Auditory signal design for automatic number plate recognition system

    NARCIS (Netherlands)

    Heydra, C.G.; Jansen, R.J.; Van Egmond, R.

    2014-01-01

    This paper focuses on the design of an auditory signal for the Automatic Number Plate Recognition system of Dutch national police. The auditory signal is designed to alert police officers of suspicious cars in their proximity, communicating priority level and location of the suspicious car and takin

  12. Automatic Recognition of Element Classes and Boundaries in the Birdsong with Variable Sequences.

    Directory of Open Access Journals (Sweden)

    Takuya Koumura

    Full Text Available Researches on sequential vocalization often require analysis of vocalizations in long continuous sounds. In such studies as developmental ones or studies across generations in which days or months of vocalizations must be analyzed, methods for automatic recognition would be strongly desired. Although methods for automatic speech recognition for application purposes have been intensively studied, blindly applying them for biological purposes may not be an optimal solution. This is because, unlike human speech recognition, analysis of sequential vocalizations often requires accurate extraction of timing information. In the present study we propose automated systems suitable for recognizing birdsong, one of the most intensively investigated sequential vocalizations, focusing on the three properties of the birdsong. First, a song is a sequence of vocal elements, called notes, which can be grouped into categories. Second, temporal structure of birdsong is precisely controlled, meaning that temporal information is important in song analysis. Finally, notes are produced according to certain probabilistic rules, which may facilitate the accurate song recognition. We divided the procedure of song recognition into three sub-steps: local classification, boundary detection, and global sequencing, each of which corresponds to each of the three properties of birdsong. We compared the performances of several different ways to arrange these three steps. As results, we demonstrated a hybrid model of a deep convolutional neural network and a hidden Markov model was effective. We propose suitable arrangements of methods according to whether accurate boundary detection is needed. Also we designed the new measure to jointly evaluate the accuracy of note classification and boundary detection. Our methods should be applicable, with small modification and tuning, to the songs in other species that hold the three properties of the sequential vocalization.

  13. Automatic Recognition of Element Classes and Boundaries in the Birdsong with Variable Sequences

    Science.gov (United States)

    Okanoya, Kazuo

    2016-01-01

    Researches on sequential vocalization often require analysis of vocalizations in long continuous sounds. In such studies as developmental ones or studies across generations in which days or months of vocalizations must be analyzed, methods for automatic recognition would be strongly desired. Although methods for automatic speech recognition for application purposes have been intensively studied, blindly applying them for biological purposes may not be an optimal solution. This is because, unlike human speech recognition, analysis of sequential vocalizations often requires accurate extraction of timing information. In the present study we propose automated systems suitable for recognizing birdsong, one of the most intensively investigated sequential vocalizations, focusing on the three properties of the birdsong. First, a song is a sequence of vocal elements, called notes, which can be grouped into categories. Second, temporal structure of birdsong is precisely controlled, meaning that temporal information is important in song analysis. Finally, notes are produced according to certain probabilistic rules, which may facilitate the accurate song recognition. We divided the procedure of song recognition into three sub-steps: local classification, boundary detection, and global sequencing, each of which corresponds to each of the three properties of birdsong. We compared the performances of several different ways to arrange these three steps. As results, we demonstrated a hybrid model of a deep convolutional neural network and a hidden Markov model was effective. We propose suitable arrangements of methods according to whether accurate boundary detection is needed. Also we designed the new measure to jointly evaluate the accuracy of note classification and boundary detection. Our methods should be applicable, with small modification and tuning, to the songs in other species that hold the three properties of the sequential vocalization. PMID:27442240

  14. Automatic Recognition of Element Classes and Boundaries in the Birdsong with Variable Sequences.

    Science.gov (United States)

    Koumura, Takuya; Okanoya, Kazuo

    2016-01-01

    Researches on sequential vocalization often require analysis of vocalizations in long continuous sounds. In such studies as developmental ones or studies across generations in which days or months of vocalizations must be analyzed, methods for automatic recognition would be strongly desired. Although methods for automatic speech recognition for application purposes have been intensively studied, blindly applying them for biological purposes may not be an optimal solution. This is because, unlike human speech recognition, analysis of sequential vocalizations often requires accurate extraction of timing information. In the present study we propose automated systems suitable for recognizing birdsong, one of the most intensively investigated sequential vocalizations, focusing on the three properties of the birdsong. First, a song is a sequence of vocal elements, called notes, which can be grouped into categories. Second, temporal structure of birdsong is precisely controlled, meaning that temporal information is important in song analysis. Finally, notes are produced according to certain probabilistic rules, which may facilitate the accurate song recognition. We divided the procedure of song recognition into three sub-steps: local classification, boundary detection, and global sequencing, each of which corresponds to each of the three properties of birdsong. We compared the performances of several different ways to arrange these three steps. As results, we demonstrated a hybrid model of a deep convolutional neural network and a hidden Markov model was effective. We propose suitable arrangements of methods according to whether accurate boundary detection is needed. Also we designed the new measure to jointly evaluate the accuracy of note classification and boundary detection. Our methods should be applicable, with small modification and tuning, to the songs in other species that hold the three properties of the sequential vocalization. PMID:27442240

  15. Speech recognition in individuals having a clinical complaint about understanding speech during noise or not

    Directory of Open Access Journals (Sweden)

    Becker, Karine Thaís

    2011-07-01

    Full Text Available Introduction: Clinical and experimental study. Individuals with a normal hearing can be jeopardized in adverse communication situations, what negatively interferes with speech clearness. Objective: check and compare the performance of normal hearing young adults who have a difficulty in understanding speech during noise or not, by making use of sentences as stimuli. Method: 50 normal hearing individuals, 21 of whom were male and 29 were female, aged between 19 and 32, were evaluated and divided into two groups: with and without a clinical complaint about understanding speech during noise. By using Portuguese Sentence Lists test, the Recognition Threshold of Sentences during Noise research was performed, through which the signal-to-noise (SN ratios were obtained. The contrasting noise was introduced at 65 dB NA. Results: the average values achieved for SN ratios in the left ear, for the group without a complaint and the group with a complaint, were respectively 6.26 dB and 3.62 dB. For the left ear, the values were -7.12 dB and -4.12 dB. A statistically significant difference was noticed in both right and left ears of the two groups. Conclusion: normal hearing individuals showing a clinical complaint about understanding speech at noisy places have more difficulty in the task to recognize sentences during noise, in comparison with the people who do not face such a difficulty. Accordingly, the customary audiologic evaluation must include tests using sentences during a contrasting noise, with a view to evaluating the speech recognition performance more reliably and efficiently. ACTRN12610000822088

  16. Automatic Recognition of Object Names in Literature

    Science.gov (United States)

    Bonnin, C.; Lesteven, S.; Derriere, S.; Oberto, A.

    2008-08-01

    SIMBAD is a database of astronomical objects that provides (among other things) their bibliographic references in a large number of journals. Currently, these references have to be entered manually by librarians who read each paper. To cope with the increasing number of papers, CDS develops a tool to assist the librarians in their work, taking advantage of the Dictionary of Nomenclature of Celestial Objects, which keeps track of object acronyms and of their origin. The program searches for object names directly in PDF documents by comparing the words with all the formats stored in the Dictionary of Nomenclature. It also searches for variable star names based on constellation names and for a large list of usual names such as Aldebaran or the Crab. Object names found in the documents often correspond to several astronomical objects. The system retrieves all possible matches, displays them with their object type given by SIMBAD, and lets the librarian make the final choice. The bibliographic reference can then be automatically added to the object identifiers in the database. Besides, the systematic usage of the Dictionary of Nomenclature, which is updated manually, permitted to automatically check it and to detect errors and inconsistencies. Last but not least, the program collects some additional information such as the position of the object names in the document (in the title, subtitle, abstract, table, figure caption...) and their number of occurrences. In the future, this will permit to calculate the 'weight' of an object in a reference and to provide SIMBAD users with an important new information, which will help them to find the most relevant papers in the object reference list.

  17. Adaptive Compensation Algorithm in Open Vocabulary Mandarin Speaker-Independent Speech Recognition

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    In speech recognition systems, the physiological characteristics of the speech production model cause the voiced sections of the speech signal to have an attenuation of approximately 20 dB per decade. Many speech recognition algorithms have been developed to solve this problem by filtering the input signal with a single-zero high pass filter. Unfortunately, this technique increases the noise energy at high frequencies above 4 kHz, which in some cases degrades the recognition accuracy. This paper solves the problem using a pre-emphasis filter in the front end of the recognizer. The aim is to develop a modified parameterization approach taking into account the whole energy zone in the spectrum to improve the performance of the existing baseline recognition system in the acoustic phase. The results show that a large vocabulary speaker-independent continuous speech recognition system using this approach has a greatly improved recognition rate.

  18. Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm

    Directory of Open Access Journals (Sweden)

    Prof. Ch.Srinivasa Kumar

    2011-08-01

    Full Text Available The results of a case study carried out while developing an automatic speaker recognition system are presented in this paper. The Vector Quantization (VQ approach is used for mapping vectors from a large vector space to a finite number of regions in that space. Each region is called a cluster and can be represented by its center called a codeword. The collection of all codewords is called a codebook. After the enrolment session, the acoustic vectors extracted from input speech of a speaker provide a set of training vectors. LBG algorithm due to Linde, Buzo and Gray is used for clustering a set of L training vectors into a set of M codebook vectors. For comparisonpurpose, the distance between each test codeword and each codeword in the master codebook is computed. The difference is used to make recognition decision. The entire coding was done in MATLAB and the system was tested for its reliability.

  19. AUTOMATIC RECOGNITION OF FREIGHT CAR NUMBER

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    This paper discusses methods for character extraction on the basis of statistical and structural features of gray-level images,and proposes a dynamic local contrast threshold method accommodating to line width.Precise locating of character string is realized by exploiting horizontal projection and character arrangements of binary images in horizontal and vertical directions respectively.Also discussed is the method for segmentation of characters in binary images,which is based on projection taking stroke width and character sizes into account.A new method for character identification is explored,which is based on compound neural networks.A complex neural network consists of two sub-nets,the first sub-net performs self-association of patterns via 2-dimentional local-connected 3-order networks,the second sub-net,linking with a locally connected BP networks,performs classification.The reliability of the network recognition is reinforced by introducing conditions for identification denial.Experiments confirm that the proposed methods possess the advantages of impressive robustness,rapid processing and high accuracy of identification.

  20. Automatic recognition of printed Oriya script

    Indian Academy of Sciences (India)

    B B Chaudhuri; U Pal; M Mitra

    2002-02-01

    This paper deals with an Optical Character Recognition (OCR) system for printed Oriya script. The development of OCR for this script is difficult because a large number of character shapes in the script have to be recognized. In the proposed system, the document image is first captured using a flat-bed scanner and then passed through different preprocessing modules like skew correction, line segmentation, zone detection, word and character segmentation etc. These modules have been developed by combining some conventional techniques with some newly proposed ones. Next, individual characters are recognized using a combination of stroke and run-number based features, along with features obtained from the concept of water overflow from a reservoir. The feature detection methods are simple and robust, and do not require preprocessing steps like thinning and pruning. A prototype of the system has been tested on a variety of printed Oriya material, and currently achieves 96.3% character level accuracy on average.

  1. Efficient Speech Recognition by Using Modular Neural Network

    Directory of Open Access Journals (Sweden)

    Dr.R.L.K.Venkateswarlu

    2011-05-01

    Full Text Available The Modular approach and Neural Network approach are well known concepts in the research and engineering community. By combining these two together, the Modular Neural Network approach is very effective in searching for solutions to complex problems of various fields. The aim of this study is the distribution of the complexity for the ambiguous words classification task on a set of modules. Each of these modules is a single Neural Network which is characterized by its high degree of specialization. The number of interfaces, and there with possibilities for filtering external acoustic – phonetic knowledge, increases a modular architecture. Modular Neural Network (MNN for speech recognition is presented with speaker dependent single word recognition in this paper. Using this approach by taking computational effort into account, the system performance can be accessed. The active performance is found maximum for MFCC while training with Modular Neural Network classifiers as 99.88%. The active performance is found maximum for LPCC while training with Modular Neural Network classifier as 99.77%. It is found that MFCC performance is superior to LPCC performance while training the speech data with Modular Neural Network classifier.

  2. Multimodal Approach for Automatic Emotion Recognition Applied to the Tension Levels Study in TV Newscasts

    Directory of Open Access Journals (Sweden)

    Moisés Henrique Ramos Pereira

    2015-12-01

    Full Text Available This article addresses a multimodal approach to automatic emotion recognition in participants of TV newscasts (presenters, reporters, commentators and others able to assist the tension levels study in narratives of events in this television genre. The methodology applies state-of-the-art computational methods to process and analyze facial expressions, as well as speech signals. The proposed approach contributes to semiodiscoursive study of TV newscasts and their enunciative praxis, assisting, for example, the identification of the communication strategy of these programs. To evaluate the effectiveness of the proposed approach was applied it in a video related to a report displayed on a Brazilian TV newscast great popularity in the state of Minas Gerais. The experimental results are promising on the recognition of emotions on the facial expressions of tele journalists and are in accordance with the distribution of audiovisual indicators extracted over a TV newscast, demonstrating the potential of the approach to support the TV journalistic discourse analysis.This article addresses a multimodal approach to automatic emotion recognition in participants of TV newscasts (presenters, reporters, commentators and others able to assist the tension levels study in narratives of events in this television genre. The methodology applies state-of-the-art computational methods to process and analyze facial expressions, as well as speech signals. The proposed approach contributes to semiodiscoursive study of TV newscasts and their enunciative praxis, assisting, for example, the identification of the communication strategy of these programs. To evaluate the effectiveness of the proposed approach was applied it in a video related to a report displayed on a Brazilian TV newscast great popularity in the state of Minas Gerais. The experimental results are promising on the recognition of emotions on the facial expressions of tele journalists and are in accordance

  3. Automatic Artist Recognition of Songs for Advanced Retrieval

    Institute of Scientific and Technical Information of China (English)

    ZHU Song-hao; LIU Yun-cai

    2008-01-01

    Automatic recognition of artists is very important in acoustic music indexing, browsing, and contentbased acoustic music retrieving, but synchronously it is still a challenging errand to extract the most representative and salient attributes to depict diversiform artists. In this paper, we developed a novel system to complete the reorganization of artist automatically. The proposed system can efficiently identify the artist's voice of a raw song by analyzing substantive features extracted from both pure music and singing song mixed with accompanying music. The experiments on different genres of songs illustrate that the proposed system is possible.

  4. Automatic Facial Expression Recognition and Operator Functional State

    Science.gov (United States)

    Blanson, Nina

    2012-01-01

    The prevalence of human error in safety-critical occupations remains a major challenge to mission success despite increasing automation in control processes. Although various methods have been proposed to prevent incidences of human error, none of these have been developed to employ the detection and regulation of Operator Functional State (OFS), or the optimal condition of the operator while performing a task, in work environments due to drawbacks such as obtrusiveness and impracticality. A video-based system with the ability to infer an individual's emotional state from facial feature patterning mitigates some of the problems associated with other methods of detecting OFS, like obtrusiveness and impracticality in integration with the mission environment. This paper explores the utility of facial expression recognition as a technology for inferring OFS by first expounding on the intricacies of OFS and the scientific background behind emotion and its relationship with an individual's state. Then, descriptions of the feedback loop and the emotion protocols proposed for the facial recognition program are explained. A basic version of the facial expression recognition program uses Haar classifiers and OpenCV libraries to automatically locate key facial landmarks during a live video stream. Various methods of creating facial expression recognition software are reviewed to guide future extensions of the program. The paper concludes with an examination of the steps necessary in the research of emotion and recommendations for the creation of an automatic facial expression recognition program for use in real-time, safety-critical missions

  5. High Range Resolution Profile Automatic Target Recognition Using Sparse Representation

    Institute of Scientific and Technical Information of China (English)

    Zhou Nuo; Chen Wei

    2010-01-01

    Sparse representation is a new signal analysis method which is receiving increasing attention in recent years.In this article,a novel scheme solving high range resolution profile automatic target recognition for ground moving targets is proposed.The sparse representation theory is applied to analyzing the components of high range resolution profiles and sparse coefficients are used to describe their features.Numerous experiments with the target type number ranging from 2 to 6 have been implemented.Results show that the proposed scheme not only provides higher recognition preciseness in real time,but also achieves more robust performance as the target type number increases.

  6. On model architecture for a children's speech recognition interactive dialog system

    OpenAIRE

    Kraleva, Radoslava; Kralev, Velin

    2016-01-01

    This report presents a general model of the architecture of information systems for the speech recognition of children. It presents a model of the speech data stream and how it works. The result of these studies and presented veins architectural model shows that research needs to be focused on acoustic-phonetic modeling in order to improve the quality of children's speech recognition and the sustainability of the systems to noise and changes in transmission environment. Another important aspe...

  7. Disordered Speech Assessment Using Automatic Methods Based on Quantitative Measures

    Directory of Open Access Journals (Sweden)

    Christine Sapienza

    2005-06-01

    Full Text Available Speech quality assessment methods are necessary for evaluating and documenting treatment outcomes of patients suffering from degraded speech due to Parkinson's disease, stroke, or other disease processes. Subjective methods of speech quality assessment are more accurate and more robust than objective methods but are time-consuming and costly. We propose a novel objective measure of speech quality assessment that builds on traditional speech processing techniques such as dynamic time warping (DTW and the Itakura-Saito (IS distortion measure. Initial results show that our objective measure correlates well with the more expensive subjective methods.

  8. A Robust Method for Speech Emotion Recognition Based on Infinite Student’s t-Mixture Model

    Directory of Open Access Journals (Sweden)

    Xinran Zhang

    2015-01-01

    Full Text Available Speech emotion classification method, proposed in this paper, is based on Student’s t-mixture model with infinite component number (iSMM and can directly conduct effective recognition for various kinds of speech emotion samples. Compared with the traditional GMM (Gaussian mixture model, speech emotion model based on Student’s t-mixture can effectively handle speech sample outliers that exist in the emotion feature space. Moreover, t-mixture model could keep robust to atypical emotion test data. In allusion to the high data complexity caused by high-dimensional space and the problem of insufficient training samples, a global latent space is joined to emotion model. Such an approach makes the number of components divided infinite and forms an iSMM emotion model, which can automatically determine the best number of components with lower complexity to complete various kinds of emotion characteristics data classification. Conducted over one spontaneous (FAU Aibo Emotion Corpus and two acting (DES and EMO-DB universal speech emotion databases which have high-dimensional feature samples and diversiform data distributions, the iSMM maintains better recognition performance than the comparisons. Thus, the effectiveness and generalization to the high-dimensional data and the outliers are verified. Hereby, the iSMM emotion model is verified as a robust method with the validity and generalization to outliers and high-dimensional emotion characters.

  9. Audibility-based predictions of speech recognition for children and adults with normal hearing.

    Science.gov (United States)

    McCreery, Ryan W; Stelmachowicz, Patricia G

    2011-12-01

    This study investigated the relationship between audibility and predictions of speech recognition for children and adults with normal hearing. The Speech Intelligibility Index (SII) is used to quantify the audibility of speech signals and can be applied to transfer functions to predict speech recognition scores. Although the SII is used clinically with children, relatively few studies have evaluated SII predictions of children's speech recognition directly. Children have required more audibility than adults to reach maximum levels of speech understanding in previous studies. Furthermore, children may require greater bandwidth than adults for optimal speech understanding, which could influence frequency-importance functions used to calculate the SII. Speech recognition was measured for 116 children and 19 adults with normal hearing. Stimulus bandwidth and background noise level were varied systematically in order to evaluate speech recognition as predicted by the SII and derive frequency-importance functions for children and adults. Results suggested that children required greater audibility to reach the same level of speech understanding as adults. However, differences in performance between adults and children did not vary across frequency bands.

  10. Automatic facial expression recognition: a discrete choice approach

    OpenAIRE

    Bierlaire, Michel

    2009-01-01

    Automatic facial expression recognition finds applications in various fields where human-machine interactions are involved. We propose a framework based on discrete choice models, where we try to forecast how a human person would evaluate the facial expression, choosing the most appropriate label among a given list. After having applied the framework successfully on static images, we investigate the possibility to apply it on video sequences.

  11. Advanced automatic target recognition for police helicopter missions

    Science.gov (United States)

    Stahl, Christoph; Schoppmann, Paul

    2000-08-01

    The results of a case study about the application of an advanced method for automatic target recognition to infrared imagery taken from police helicopter missions are presented. The method consists of the following steps: preprocessing, classification, fusion, postprocessing and tracking, and combines the three paradigms image pyramids, neural networks and bayesian nets. The technology has been developed using a variety of different scenes typical for military aircraft missions. Infrared cameras have been in use for several years at the Bavarian police helicopter forces and are highly valuable for night missions. Several object classes like 'persons' or 'vehicles' are tested and the possible discrimination between persons and animals is shown. The analysis of complex scenes with hidden objects and clutter shows the potentials and limitations of automatic target recognition for real-world tasks. Several display concepts illustrate the achievable improvement of the situation awareness. The similarities and differences between various mission types concerning object variability, time constraints, consequences of false alarms, etc. are discussed. Typical police actions like searching for missing persons or runaway criminals illustrate the advantages of automatic target recognition. The results demonstrate the possible operational benefits for the helicopter crew. Future work will include performance evaluation issues and a system integration concept for the target platform.

  12. Automatic Recognition of Facial Actions in Spontaneous Expressions

    Directory of Open Access Journals (Sweden)

    Marian Stewart Bartlett

    2006-09-01

    Full Text Available Spontaneous facial expressions differ from posed expressions in both which muscles are moved, and in the dynamics of the movement. Advances in the field of automatic facial expression measurement will require development and assessment on spontaneous behavior. Here we present preliminary results on a task of facial action detection in spontaneous facial expressions. We employ a user independent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS. The system automatically detects frontal faces in the video stream and coded each frame with respect to 20 Action units. The approach applies machine learning methods such as support vector machines and AdaBoost, to texture-based image representations. The output margin for the learned classifiers predicts action unit intensity. Frame-by-frame intensity measurements will enable investigations into facial expression dynamics which were previously intractable by human coding.

  13. Automatic anatomy recognition on CT images with pathology

    Science.gov (United States)

    Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

    2016-03-01

    Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

  14. A system of automatic speaker recognition on a minicomputer

    International Nuclear Information System (INIS)

    This study describes a system of automatic speaker recognition using the pitch of the voice. The pre-treatment consists in the extraction of the speakers' discriminating characteristics taken from the pitch. The programme of recognition gives, firstly, a preselection and then calculates the distance between the speaker's characteristics to be recognized and those of the speakers already recorded. An experience of recognition has been realized. It has been undertaken with 15 speakers and included 566 tests spread over an intermittent period of four months. The discriminating characteristics used offer several interesting qualities. The algorithms concerning the measure of the characteristics on one hand, the speakers' classification on the other hand, are simple. The results obtained in real time with a minicomputer are satisfactory. Furthermore they probably could be improved if we considered other speaker's discriminating characteristics but this was unfortunately not in our possibilities. (author)

  15. Robust Digital Speech Watermarking For Online Speaker Recognition

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Nematollahi

    2015-01-01

    Full Text Available A robust and blind digital speech watermarking technique has been proposed for online speaker recognition systems based on Discrete Wavelet Packet Transform (DWPT and multiplication to embed the watermark in the amplitudes of the wavelet’s subbands. In order to minimize the degradation effect of the watermark, these subbands are selected where less speaker-specific information was available (500 Hz–3500 Hz and 6000 Hz–7000 Hz. Experimental results on Texas Instruments Massachusetts Institute of Technology (TIMIT, Massachusetts Institute of Technology (MIT, and Mobile Biometry (MOBIO show that the degradation for speaker verification and identification is 1.16% and 2.52%, respectively. Furthermore, the proposed watermark technique can provide enough robustness against different signal processing attacks.

  16. Impact of noise and other factors on speech recognition in anaesthesia

    DEFF Research Database (Denmark)

    Alapetite, Alexandre

    2008-01-01

    operations. Objective: The aim of the experiment is to evaluate the relative impact of several factors affecting speech recognition when used in operating rooms, such as the type or loudness of background noises, type of microphone, type of recognition mode (free speech versus command mode), and type......Introduction: Speech recognition is currently being deployed in medical and anaesthesia applications. This article is part of a project to investigate and further develop a prototype of a speech-input interface in Danish for an electronic anaesthesia patient record, to be used in real time during...... of training. Methods: Eight volunteers read aloud a total of about 3 600 typical short anaesthesia comments to be transcribed by a continuous speech recognition system. Background noises were collected in an operating room and reproduced. A regression analysis and descriptive statistics were done to evaluate...

  17. Evaluation of automatic face recognition for automatic border control on actual data recorded of travellers at Schiphol Airport

    NARCIS (Netherlands)

    Spreeuwers, L.J.; Hendrikse, A.J.; Gerritsen, K.J.; Brömme, A.; Busch, C.

    2012-01-01

    Automatic border control at airports using automated facial recognition for checking the passport is becoming more and more common. A problem is that it is not clear how reliable these automatic gates are. Very few independent studies exist that assess the reliability of automated facial recognition

  18. Benefits of spatial hearing to speech recognition in young people with normal hearing

    Institute of Scientific and Technical Information of China (English)

    SONG Peng-long; LI Hui-jun; WANG Ning-yu

    2011-01-01

    Background Many factors interfering with a listener attempting to grasp speech in noisy environments.The spatial hearing by which speech and noise can be spatially separated may play a crucial role in speech recognition in the presence of competing noise.This study aimed to assess whether,and to what degree,spatial hearing benefit speech recognition in young normal-hearing participants in both quiet and noisy environments.Methods Twenty-eight young participants were tested by Mandarin Hearing In Noise Test (MHINT) in quiet and noisy environments.The assessment method used was characterized by modifications of speech and noise configurations,as well as by changes of speech presentation mode.The benefit of spatial hearing was measured by speech recognition threshold (SRT) variation between speech condition 1 (SC1) and speech condition 2 (SC2).Results There was no significant difference found in the SRT between SC1 and SC2 in quiet.SRT in SC1 was about 4.2 dB lower than that in SC2,both in speech-shaped and four-babble noise conditions.SRTs measured in both SC1 and SC2 were lower in the speech-shaped noise condition than in the four-babble noise condition.Conclusion Spatial hearing in young normal-hearing participants contribute to speech recognition in noisy environments,but provide no benefit to speech recognition in quiet environments,which may be due to the offset of auditory extrinsic redundancy against the lack of spatial hearing.

  19. Integranting prosodic information into a speech recogniser

    OpenAIRE

    López Soto, María Teresa

    2001-01-01

    In the last decade there has been an increasing tendency to incorporate language engineering strategies into speech technology. This technique combines linguistic and mathematical information in different applications: machine translation, natural language processing, speech synthesis and automatic speech recognition (ASR). In the field of speech synthesis, this hybrid approach (linguistic and mathematical/statistical) has led to the design of efficient models for reproducin...

  20. Error Rates in Users of Automatic Face Recognition Software.

    Directory of Open Access Journals (Sweden)

    David White

    Full Text Available In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated 'candidate lists' selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers-who use the system in their daily work-and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced "facial examiners" outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems-potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems.

  1. A Support Vector Machine-Based Dynamic Network for Visual Speech Recognition Applications

    Directory of Open Access Journals (Sweden)

    Mihaela Gordan

    2002-11-01

    Full Text Available Visual speech recognition is an emerging research field. In this paper, we examine the suitability of support vector machines for visual speech recognition. Each word is modeled as a temporal sequence of visemes corresponding to the different phones realized. One support vector machine is trained to recognize each viseme and its output is converted to a posterior probability through a sigmoidal mapping. To model the temporal character of speech, the support vector machines are integrated as nodes into a Viterbi lattice. We test the performance of the proposed approach on a small visual speech recognition task, namely the recognition of the first four digits in English. The word recognition rate obtained is at the level of the previous best reported rates.

  2. Automatic integration of social information in emotion recognition.

    Science.gov (United States)

    Mumenthaler, Christian; Sander, David

    2015-04-01

    This study investigated the automaticity of the influence of social inference on emotion recognition. Participants were asked to recognize dynamic facial expressions of emotion (fear or anger in Experiment 1 and blends of fear and surprise or of anger and disgust in Experiment 2) in a target face presented at the center of a screen while a subliminal contextual face appearing in the periphery expressed an emotion (fear or anger) or not (neutral) and either looked at the target face or not. Results of Experiment 1 revealed that recognition of the target emotion of fear was improved when a subliminal angry contextual face gazed toward-rather than away from-the fearful face. We replicated this effect in Experiment 2, in which facial expression blends of fear and surprise were more often and more rapidly categorized as expressing fear when the subliminal contextual face expressed anger and gazed toward-rather than away from-the target face. With the contextual face appearing for 30 ms in total, including only 10 ms of emotion expression, and being immediately masked, our data provide the first evidence that social influence on emotion recognition can occur automatically. PMID:25688908

  3. Advancing Electromyographic Continuous Speech Recognition: Signal Preprocessing and Modeling

    OpenAIRE

    Wand, Michael

    2014-01-01

    Speech is the natural medium of human communication, but audible speech can be overheard by bystanders and excludes speech-disabled people. This work presents a speech recognizer based on surface electromyography, where electric potentials of the facial muscles are captured by surface electrodes, allowing speech to be processed nonacoustically. A system which was state-of-the-art at the beginning of this thesis is substantially improved in terms of accuracy, flexibility, and robustness.

  4. Speech recognition interference by the temporal and spectral properties of a single competing talker.

    Science.gov (United States)

    Fogerty, Daniel; Xu, Jiaqian

    2016-08-01

    This study investigated how speech recognition during speech-on-speech masking may be impaired due to the interaction between amplitude modulations of the target and competing talker. Young normal-hearing adults were tested in a competing talker paradigm where the target and/or competing talker was processed to primarily preserve amplitude modulation cues. Effects of talker sex and linguistic interference were also examined. Results suggest that performance patterns for natural speech-on-speech conditions are largely consistent with the same masking patterns observed for signals primarily limited to temporal amplitude modulations. However, results also suggest a role for spectral cues in talker segregation and linguistic competition. PMID:27586780

  5. I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance.

    Directory of Open Access Journals (Sweden)

    Simone Hantke

    Full Text Available We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers, six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps, and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR, it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient.

  6. I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance.

    Science.gov (United States)

    Hantke, Simone; Weninger, Felix; Kurle, Richard; Ringeval, Fabien; Batliner, Anton; Mousa, Amr El-Desoky; Schuller, Björn

    2016-01-01

    We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient. PMID:27176486

  7. Significance of parametric spectral ratio methods in detection and recognition of whispered speech

    Science.gov (United States)

    Mathur, Arpit; Reddy, Shankar M.; Hegde, Rajesh M.

    2012-12-01

    In this article the significance of a new parametric spectral ratio method that can be used to detect whispered speech segments within normally phonated speech is described. Adaptation methods based on the maximum likelihood linear regression (MLLR) are then used to realize a mismatched train-test style speech recognition system. This proposed parametric spectral ratio method computes a ratio spectrum of the linear prediction (LP) and the minimum variance distortion-less response (MVDR) methods. The smoothed ratio spectrum is then used to detect whispered segments of speech within neutral speech segments effectively. The proposed LP-MVDR ratio method exhibits robustness at different SNRs as indicated by the whisper diarization experiments conducted on the CHAINS and the cell phone whispered speech corpus. The proposed method also performs reasonably better than the conventional methods for whisper detection. In order to integrate the proposed whisper detection method into a conventional speech recognition engine with minimal changes, adaptation methods based on the MLLR are used herein. The hidden Markov models corresponding to neutral mode speech are adapted to the whispered mode speech data in the whispered regions as detected by the proposed ratio method. The performance of this method is first evaluated on whispered speech data from the CHAINS corpus. The second set of experiments are conducted on the cell phone corpus of whispered speech. This corpus is collected using a set up that is used commercially for handling public transactions. The proposed whisper speech recognition system exhibits reasonably better performance when compared to several conventional methods. The results shown indicate the possibility of a whispered speech recognition system for cell phone based transactions.

  8. Automatic radar target recognition of objects falling on railway tracks

    International Nuclear Information System (INIS)

    This paper presents an automatic radar target recognition procedure based on complex resonances using the signals provided by ultra-wideband radar. This procedure is dedicated to detection and identification of objects lying on railway tracks. For an efficient complex resonance extraction, a comparison between several pole extraction methods is illustrated. Therefore, preprocessing methods are presented aiming to remove most of the erroneous poles interfering with the discrimination scheme. Once physical poles are determined, a specific discrimination technique is introduced based on the Euclidean distances. Both simulation and experimental results are depicted showing an efficient discrimination of different targets including guided transport passengers

  9. Facilities of different methods of automatic recognition of sleep stages

    Directory of Open Access Journals (Sweden)

    Erofeev A.E.

    2012-06-01

    Full Text Available

    The gole of the research is to consider the information content in application of different fractal methods of deterministic chaos to the automated recognition of sleep phases in computer electroencephalogram (EEG. The Hurst normalized range method, the method of calculating of Grassberger — Procaccia correlation integral and the approximated entropy method are used during the research. The research reveals that a hypnogram can be obtained. It’s possible if appropriate parameters of the methods indicated above are used, as well as the necessary normalization of the original data and averaging the results. A hypnogram has a total coincidence of defned sleep phases for half of the epochs which are recorded on EEG. Current methods of automatic recognition of sleep stages based on the deterministic chaos allow to reduce signifcantly the time of interpretation of polysomnographic recording and reduce the number of channels through which parameters of sleep are registrated.

  10. CCD camera automatic calibration technology and ellipse recognition algorithm

    Institute of Scientific and Technical Information of China (English)

    Changku Sun; Xiaodong Zhang; Yunxia Qu

    2005-01-01

    A novel two-dimensional (2D) pattern used in camera calibration is presented. With one feature circle located at the center, an array of circles is photo-etched on this pattern. An ellipse recognition algorithm is proposed to implement the acquisition of interest calibration points without human intervention. According to the circle arrangement of the pattern, the relation between three-dimensional (3D) and 2D coordinates of these points can be established automatically and accurately. These calibration points are computed for intrinsic parameters calibration of charge-coupled device (CCD) camera with Tsai method. A series of experiments have shown that the algorithm is robust and reliable with the calibration error less than 0.4 pixel. This new calibration pattern and ellipse recognition algorithm can be widely used in computer vision.

  11. Automatic Vehicle License Recognition Based on Video Vehicular Detection System

    Institute of Scientific and Technical Information of China (English)

    YANG Zhaoxuan; CHEN Yang; HE Yinghua; WU Jun

    2006-01-01

    Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system.Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algorithm based on Markov random field model is presented.Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold.The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%.When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system's requirement even under the noisy circumstance or uneven illumination.

  12. A Comparative Study: Gammachirp Wavelets and Auditory Filter Using Prosodic Features of Speech Recognition In Noisy Environment

    Directory of Open Access Journals (Sweden)

    Hajer Rahali

    2014-04-01

    Full Text Available Modern automatic speech recognition (ASR systems typically use a bank of linear filters as the first step in performing frequency analysis of speech. On the other hand, the cochlea, which is responsible for frequency analysis in the human auditory system, is known to have a compressive non-linear frequency response which depends on input stimulus level. It will be shown in this paper that it presents a new method on the use of the gammachirp auditory filter based on a continuous wavelet analysis. The essential characteristic of this model is that it proposes an analysis by wavelet packet transformation on the frequency bands that come closer the critical bands of the ear that differs from the existing model based on an analysis by a short term Fourier transformation (STFT. The prosodic features such as pitch, formant frequency, jitter and shimmer are extracted from the fundamental frequency contour and added to baseline spectral features, specifically, Mel Frequency Cepstral Coefficients (MFCC for human speech, Gammachirp Filterbank Cepstral Coefficient (GFCC and Gammachirp Wavelet Frequency Cepstral Coefficient (GWFCC. The results show that the gammachirp wavelet gives results that are comparable to ones obtained by MFCC and GFCC. Experimental results show the best performance of this architecture. This paper implements the GW and examines its application to a specific example of speech. Implications for noise robust speech analysis are also discussed within AURORA databases.

  13. A speech recognition system for data collection in precision agriculture

    Science.gov (United States)

    Dux, David Lee

    Agricultural producers have shown interest in collecting detailed, accurate, and meaningful field data through field scouting, but scouting is labor intensive. They use yield monitor attachments to collect weed and other field data while driving equipment. However, distractions from using a keyboard or buttons while driving can lead to driving errors or missed data points. At Purdue University, researchers have developed an ASR system to allow equipment operators to collect georeferenced data while keeping hands and eyes on the machine during harvesting and to ease georeferencing of data collected during scouting. A notebook computer retrieved locations from a GPS unit and displayed and stored data in Excel. A headset microphone with a single earphone collected spoken input while allowing the operator to hear outside sounds. One-, two-, or three-word commands activated appropriate VBA macros. Four speech recognition products were chosen based on hardware requirements and ability to add new terms. After training, speech recognition accuracy was 100% for Kurzweil VoicePlus and Verbex Listen for the 132 vocabulary words tested, during tests walking outdoors or driving an ATV. Scouting tests were performed by carrying the system in a backpack while walking in soybean fields. The system recorded a point or a series of points with each utterance. Boundaries of points showed problem areas in the field and single points marked rocks and field corners. Data were displayed as an Excel chart to show a real-time map as data were collected. The information was later displayed in a GIS over remote sensed field images. Field corners and areas of poor stand matched, with voice data explaining anomalies in the image. The system was tested during soybean harvest by using voice to locate weed patches. A harvester operator with little computer experience marked points by voice when the harvester entered and exited weed patches or areas with poor crop stand. The operator found the

  14. Visual abilities are important for auditory-only speech recognition: evidence from autism spectrum disorder.

    Science.gov (United States)

    Schelinski, Stefanie; Riedel, Philipp; von Kriegstein, Katharina

    2014-12-01

    In auditory-only conditions, for example when we listen to someone on the phone, it is essential to fast and accurately recognize what is said (speech recognition). Previous studies have shown that speech recognition performance in auditory-only conditions is better if the speaker is known not only by voice, but also by face. Here, we tested the hypothesis that such an improvement in auditory-only speech recognition depends on the ability to lip-read. To test this we recruited a group of adults with autism spectrum disorder (ASD), a condition associated with difficulties in lip-reading, and typically developed controls. All participants were trained to identify six speakers by name and voice. Three speakers were learned by a video showing their face and three others were learned in a matched control condition without face. After training, participants performed an auditory-only speech recognition test that consisted of sentences spoken by the trained speakers. As a control condition, the test also included speaker identity recognition on the same auditory material. The results showed that, in the control group, performance in speech recognition was improved for speakers known by face in comparison to speakers learned in the matched control condition without face. The ASD group lacked such a performance benefit. For the ASD group auditory-only speech recognition was even worse for speakers known by face compared to speakers not known by face. In speaker identity recognition, the ASD group performed worse than the control group independent of whether the speakers were learned with or without face. Two additional visual experiments showed that the ASD group performed worse in lip-reading whereas face identity recognition was within the normal range. The findings support the view that auditory-only communication involves specific visual mechanisms. Further, they indicate that in ASD, speaker-specific dynamic visual information is not available to optimize auditory

  15. Introduction and Overview of the Vicens-Reddy Speech Recognition System.

    Science.gov (United States)

    Kameny, Iris; Ritea, H.

    The Vicens-Reddy System is unique in the sense that it approaches the problem of speech recognition as a whole, rather than treating particular aspects of the problems as in previous attempts. For example, where earlier systems treated only segmentation of speech into phoneme groups, or detected phonemes in a given context, the Vicens-Reddy System…

  16. Entrance C - New Automatic Number Plate Recognition System

    CERN Multimedia

    2013-01-01

    Entrance C (Satigny) is now equipped with a latest-generation Automatic Number Plate Recognition (ANPR) system and a fast-action road gate.   During the month of August, Entrance C will be continuously open from 7.00 a.m. to 7.00 p.m. (working days only). The security guards will open the gate as usual from 7.00 a.m. to 9.00 a.m. and from 5.00 p.m. to 7.00 p.m. For the rest of the working day (9.00 a.m. to 5.00 p.m.) the gate will operate automatically. Please observe the following points:       Stop at the STOP sign on the ground     Position yourself next to the card reader for optimal recognition     Motorcyclists must use their CERN card     Cyclists may not activate the gate and should use the bicycle turnstile     Keep a safe distance from the vehicle in front of you   If access is denied, please check that your vehicle regist...

  17. Influences of Infant-Directed Speech on Early Word Recognition

    Science.gov (United States)

    Singh, Leher; Nestor, Sarah; Parikh, Chandni; Yull, Ashley

    2009-01-01

    When addressing infants, many adults adopt a particular type of speech, known as infant-directed speech (IDS). IDS is characterized by exaggerated intonation, as well as reduced speech rate, shorter utterance duration, and grammatical simplification. It is commonly asserted that IDS serves in part to facilitate language learning. Although…

  18. Is Listening in Noise Worth It? The Neurobiology of Speech Recognition in Challenging Listening Conditions.

    Science.gov (United States)

    Eckert, Mark A; Teubner-Rhodes, Susan; Vaden, Kenneth I

    2016-01-01

    This review examines findings from functional neuroimaging studies of speech recognition in noise to provide a neural systems level explanation for the effort and fatigue that can be experienced during speech recognition in challenging listening conditions. Neuroimaging studies of speech recognition consistently demonstrate that challenging listening conditions engage neural systems that are used to monitor and optimize performance across a wide range of tasks. These systems appear to improve speech recognition in younger and older adults, but sustained engagement of these systems also appears to produce an experience of effort and fatigue that may affect the value of communication. When considered in the broader context of the neuroimaging and decision making literature, the speech recognition findings from functional imaging studies indicate that the expected value, or expected level of speech recognition given the difficulty of listening conditions, should be considered when measuring effort and fatigue. The authors propose that the behavioral economics or neuroeconomics of listening can provide a conceptual and experimental framework for understanding effort and fatigue that may have clinical significance. PMID:27355759

  19. Combined Feature Extraction Techniques and Naive Bayes Classifier for Speech Recognition

    Directory of Open Access Journals (Sweden)

    Sonia Sunny

    2013-07-01

    Full Text Available Speech processing and consequent recognition are im portant areas of Digital Signal Processing since speech allows people to communicate more natu -rally and efficiently. In this work, a speech recognition system is developed for re-cogni zing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech a nd hence feature extraction method plays an important role in speech recognition. Here, front e nd processing for extracting the features is per-formed using two wavelet based methods namely D iscrete Wavelet Transforms (DWT and Wavelet Packet Decomposition (WPD. Naive Bayes cla ssifier is used for classification purpose. After classification using Naive Bayes classifier, DWT produced a recognition accuracy of 83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvemen ts in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decompositio n (DWPD is introduced which utilizes the hy-brid features of both DWT and WPD. The perfo rmance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.

  20. Influence of native and non-native multitalker babble on speech recognition in noise

    Directory of Open Access Journals (Sweden)

    Chandni Jain

    2014-03-01

    Full Text Available The aim of the study was to assess speech recognition in noise using multitalker babble of native and non-native language at two different signal to noise ratios. The speech recognition in noise was assessed on 60 participants (18 to 30 years with normal hearing sensitivity, having Malayalam and Kannada as their native language. For this purpose, 6 and 10 multitalker babble were generated in Kannada and Malayalam language. Speech recognition was assessed for native listeners of both the languages in the presence of native and nonnative multitalker babble. Results showed that the speech recognition in noise was significantly higher for 0 dB signal to noise ratio (SNR compared to -3 dB SNR for both the languages. Performance of Kannada Listeners was significantly higher in the presence of native (Kannada babble compared to non-native babble (Malayalam. However, this was not same with the Malayalam listeners wherein they performed equally well with native (Malayalam as well as non-native babble (Kannada. The results of the present study highlight the importance of using native multitalker babble for Kannada listeners in lieu of non-native babble and, considering the importance of each SNR for estimating speech recognition in noise scores. Further research is needed to assess speech recognition in Malayalam listeners in the presence of other non-native backgrounds of various types.

  1. Adoption of Speech Recognition Technology in Community Healthcare Nursing.

    Science.gov (United States)

    Al-Masslawi, Dawood; Block, Lori; Ronquillo, Charlene

    2016-01-01

    Adoption of new health information technology is shown to be challenging. However, the degree to which new technology will be adopted can be predicted by measures of usefulness and ease of use. In this work these key determining factors are focused on for design of a wound documentation tool. In the context of wound care at home, consistent with evidence in the literature from similar settings, use of Speech Recognition Technology (SRT) for patient documentation has shown promise. To achieve a user-centred design, the results from a conducted ethnographic fieldwork are used to inform SRT features; furthermore, exploratory prototyping is used to collect feedback about the wound documentation tool from home care nurses. During this study, measures developed for healthcare applications of the Technology Acceptance Model will be used, to identify SRT features that improve usefulness (e.g. increased accuracy, saving time) or ease of use (e.g. lowering mental/physical effort, easy to remember tasks). The identified features will be used to create a low fidelity prototype that will be evaluated in future experiments.

  2. Adoption of Speech Recognition Technology in Community Healthcare Nursing.

    Science.gov (United States)

    Al-Masslawi, Dawood; Block, Lori; Ronquillo, Charlene

    2016-01-01

    Adoption of new health information technology is shown to be challenging. However, the degree to which new technology will be adopted can be predicted by measures of usefulness and ease of use. In this work these key determining factors are focused on for design of a wound documentation tool. In the context of wound care at home, consistent with evidence in the literature from similar settings, use of Speech Recognition Technology (SRT) for patient documentation has shown promise. To achieve a user-centred design, the results from a conducted ethnographic fieldwork are used to inform SRT features; furthermore, exploratory prototyping is used to collect feedback about the wound documentation tool from home care nurses. During this study, measures developed for healthcare applications of the Technology Acceptance Model will be used, to identify SRT features that improve usefulness (e.g. increased accuracy, saving time) or ease of use (e.g. lowering mental/physical effort, easy to remember tasks). The identified features will be used to create a low fidelity prototype that will be evaluated in future experiments. PMID:27332294

  3. Effect of Speaker Age on Speech Recognition and Perceived Listening Effort in Older Adults with Hearing Loss

    Science.gov (United States)

    McAuliffe, Megan J.; Wilding, Phillipa J.; Rickard, Natalie A.; O'Beirne, Greg A.

    2012-01-01

    Purpose: Older adults exhibit difficulty understanding speech that has been experimentally degraded. Age-related changes to the speech mechanism lead to natural degradations in signal quality. We tested the hypothesis that older adults with hearing loss would exhibit declines in speech recognition when listening to the speech of older adults,…

  4. An analytical approach to photonic reservoir computing - a network of SOA's - for noisy speech recognition

    Science.gov (United States)

    Salehi, Mohammad Reza; Abiri, Ebrahim; Dehyadegari, Louiza

    2013-10-01

    This paper seeks to investigate an approach of photonic reservoir computing for optical speech recognition on an examination isolated digit recognition task. An analytical approach in photonic reservoir computing is further drawn on to decrease time consumption, compared to numerical methods; which is very important in processing large signals such as speech recognition. It is also observed that adjusting reservoir parameters along with a good nonlinear mapping of the input signal into the reservoir, analytical approach, would boost recognition accuracy performance. Perfect recognition accuracy (i.e. 100%) can be achieved for noiseless speech signals. For noisy signals with 0-10 db of signal to noise ratios, however, the accuracy ranges observed varied between 92% and 98%. In fact, photonic reservoir application demonstrated 9-18% improvement compared to classical reservoir networks with hyperbolic tangent nodes.

  5. Combining Semantic and Acoustic Features for Valence and Arousal Recognition in Speech

    DEFF Research Database (Denmark)

    Karadogan, Seliz; Larsen, Jan

    2012-01-01

    The recognition of affect in speech has attracted a lot of interest recently; especially in the area of cognitive and computer sciences. Most of the previous studies focused on the recognition of basic emotions (such as happiness, sadness and anger) using categorical approach. Recently, the focus...... has been shifting towards dimensional affect recognition based on the idea that emotional states are not independent from one another but related in a systematic manner. In this paper, we design a continuous dimensional speech affect recognition model that combines acoustic and semantic features. We...... show that combining semantic and acoustic information for dimensional speech recognition improves the results. Moreover, we show that valence is better estimated using semantic features while arousal is better estimated using acoustic features....

  6. Prediction of Speech Recognition in Cochlear Implant Users by Adapting Auditory Models to Psychophysical Data

    Directory of Open Access Journals (Sweden)

    Svante Stadler

    2009-01-01

    Full Text Available Users of cochlear implants (CIs vary widely in their ability to recognize speech in noisy conditions. There are many factors that may influence their performance. We have investigated to what degree it can be explained by the users' ability to discriminate spectral shapes. A speech recognition task has been simulated using both a simple and a complex models of CI hearing. The models were individualized by adapting their parameters to fit the results of a spectral discrimination test. The predicted speech recognition performance was compared to experimental results, and they were significantly correlated. The presented framework may be used to simulate the effects of changing the CI encoding strategy.

  7. Cross-modal enhancement of the MMN to speech-sounds indicates early and automatic integration of letters and speech-sounds.

    Science.gov (United States)

    Froyen, Dries; Van Atteveldt, Nienke; Bonte, Milene; Blomert, Leo

    2008-01-01

    Recently brain imaging evidence indicated that letter/speech-sound integration, necessary for establishing fluent reading, takes place in auditory association areas and that the integration is influenced by stimulus onset asynchrony (SOA) between the letter and the speech-sound. In the present study, we used a specific ERP measure known for its automatic character, the mismatch negativity (MMN), to investigate the time course and automaticity of letter/speech-sound integration. We studied the effect of visual letters and SOA on the MMN elicited by a deviant speech-sound. We found a clear enhancement of the MMN by simultaneously presenting a letter, but without changing the auditory stimulation. This enhancement diminishes linearly with increasing SOA. These results suggest that letters and speech-sounds are processed as compound stimuli early and automatically in the auditory association cortex of fluent readers and that this processing is strongly dependent on timing.

  8. Relative Contributions of Spectral and Temporal Cues for Speech Recognition in Patients with Sensorineural Hearing Loss

    Institute of Scientific and Technical Information of China (English)

    XU Li; ZHOU Ning; Rebecca Brashears; Katherine Rife

    2008-01-01

    The present study was designed to examine speech recognition in patients with sensorineural hearing loss when the temporal and spectral information in the speech signals were co-varied. Four subjects with mild to moderate sensorineural hearing loss were recruited to participate in consonant and vowel recognition tests that used speech stimuli processed through a noise-excited voeoder. The number of channels was varied between 2 and 32, which defined spectral information. The lowpass cutoff frequency of the temporal envelope extractor was varied from 1 to 512 Hz, which defined temporal information. Results indicate that performance of subjects with sensorineural heating loss varied tremendously among the subjects. For consonant recognition, patterns of relative contributions of spectral and temporal information were similar to those in normal-hearing subjects. The utility of temporal envelope information appeared to be normal in the hearing-impaired listeners. For vowel recognition, which depended predominately on spectral information, the performance plateau was achieved with numbers of channels as high as 16-24, much higher than expected, given that the frequency selectivity in patients with sensorineural hearing loss might be compromised. In order to understand the mechanisms on how hearing-impaired listeners utilize spectral and temporal cues for speech recognition, future studies that involve a large sample of patients with sensorineural hearing loss will be necessary to elucidate the relationship between frequency selectivity as well as central processing capability and speech recognition performance using vocoded signals.

  9. Speech recognition materials and ceiling effects: considerations for cochlear implant programs.

    Science.gov (United States)

    Gifford, René H; Shallop, Jon K; Peterson, Anna Mary

    2008-01-01

    Cochlear implant recipients have demonstrated remarkable increases in speech perception since US FDA approval was granted in 1984. Improved performance is due to a number of factors including improved cochlear implant technology, evolving speech coding strategies, and individuals with increasingly more residual hearing receiving implants. Despite this evolution, the same recommendations for pre- and postimplant speech recognition testing have been in place for over 10 years in the United States. To determine whether new recommendations are warranted, speech perception performance was assessed for 156 adult, postlingually deafened implant recipients as well as 50 hearing aid users on monosyllabic word recognition (CNC) and sentence recognition in quiet (HINT and AzBio sentences) and in noise (BKB-SIN). Results demonstrated that for HINT sentences in quiet, 28% of the subjects tested achieved maximum performance of 100% correct and that scores did not agree well with monosyllables (CNC) or sentence recognition in noise (BKB-SIN). For a more difficult sentence recognition material (AzBio), only 0.7% of the subjects achieved 100% performance and scores were in much better agreement with monosyllables and sentence recognition in noise. These results suggest that more difficult materials are needed to assess speech perception performance of postimplant patients - and perhaps also for determining implant candidacy. PMID:18212519

  10. Effects of Semantic Context and Fundamental Frequency Contours on Mandarin Speech Recognition by Second Language Learners

    Science.gov (United States)

    Zhang, Linjun; Li, Yu; Wu, Han; Li, Xin; Shu, Hua; Zhang, Yang; Li, Ping

    2016-01-01

    Speech recognition by second language (L2) learners in optimal and suboptimal conditions has been examined extensively with English as the target language in most previous studies. This study extended existing experimental protocols (Wang et al., 2013) to investigate Mandarin speech recognition by Japanese learners of Mandarin at two different levels (elementary vs. intermediate) of proficiency. The overall results showed that in addition to L2 proficiency, semantic context, F0 contours, and listening condition all affected the recognition performance on the Mandarin sentences. However, the effects of semantic context and F0 contours on L2 speech recognition diverged to some extent. Specifically, there was significant modulation effect of listening condition on semantic context, indicating that L2 learners made use of semantic context less efficiently in the interfering background than in quiet. In contrast, no significant modulation effect of listening condition on F0 contours was found. Furthermore, there was significant interaction between semantic context and F0 contours, indicating that semantic context becomes more important for L2 speech recognition when F0 information is degraded. None of these effects were found to be modulated by L2 proficiency. The discrepancy in the effects of semantic context and F0 contours on L2 speech recognition in the interfering background might be related to differences in processing capacities required by the two types of information in adverse listening conditions. PMID:27378997

  11. Feature Fusion Algorithm for Multimodal Emotion Recognition from Speech and Facial Expression Signal

    Directory of Open Access Journals (Sweden)

    Han Zhiyan

    2016-01-01

    Full Text Available In order to overcome the limitation of single mode emotion recognition. This paper describes a novel multimodal emotion recognition algorithm, and takes speech signal and facial expression signal as the research subjects. First, fuse the speech signal feature and facial expression signal feature, get sample sets by putting back sampling, and then get classifiers by BP neural network (BPNN. Second, measure the difference between two classifiers by double error difference selection strategy. Finally, get the final recognition result by the majority voting rule. Experiments show the method improves the accuracy of emotion recognition by giving full play to the advantages of decision level fusion and feature level fusion, and makes the whole fusion process close to human emotion recognition more, with a recognition rate 90.4%.

  12. Recognition of voice commands using adaptation of foreign language speech recognizer via selection of phonetic transcriptions

    Science.gov (United States)

    Maskeliunas, Rytis; Rudzionis, Vytautas

    2011-06-01

    In recent years various commercial speech recognizers have become available. These recognizers provide the possibility to develop applications incorporating various speech recognition techniques easily and quickly. All of these commercial recognizers are typically targeted to widely spoken languages having large market potential; however, it may be possible to adapt available commercial recognizers for use in environments where less widely spoken languages are used. Since most commercial recognition engines are closed systems the single avenue for the adaptation is to try set ways for the selection of proper phonetic transcription methods between the two languages. This paper deals with the methods to find the phonetic transcriptions for Lithuanian voice commands to be recognized using English speech engines. The experimental evaluation showed that it is possible to find phonetic transcriptions that will enable the recognition of Lithuanian voice commands with recognition accuracy of over 90%.

  13. Automatic recognition of offensive team formation in american football plays

    KAUST Repository

    Atmosukarto, Indriyati

    2013-06-01

    Compared to security surveillance and military applications, where automated action analysis is prevalent, the sports domain is extremely under-served. Most existing software packages for sports video analysis require manual annotation of important events in the video. American football is the most popular sport in the United States, however most game analysis is still done manually. Line of scrimmage and offensive team formation recognition are two statistics that must be tagged by American Football coaches when watching and evaluating past play video clips, a process which takes many man hours per week. These two statistics are also the building blocks for more high-level analysis such as play strategy inference and automatic statistic generation. In this paper, we propose a novel framework where given an American football play clip, we automatically identify the video frame in which the offensive team lines in formation (formation frame), the line of scrimmage for that play, and the type of player formation the offensive team takes on. The proposed framework achieves 95% accuracy in detecting the formation frame, 98% accuracy in detecting the line of scrimmage, and up to 67% accuracy in classifying the offensive team\\'s formation. To validate our framework, we compiled a large dataset comprising more than 800 play-clips of standard and high definition resolution from real-world football games. This dataset will be made publicly available for future comparison. © 2013 IEEE.

  14. An Automatic Number Plate Recognition System under Image Processing

    Directory of Open Access Journals (Sweden)

    Sarbjit Kaur

    2016-03-01

    Full Text Available Automatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image , it display the number plate information into text. Mainly the ANPR system consists of 4 phases: - Acquisition of Vehicle Image and Pre-Processing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. The overall accuracy and efficiency of whole ANPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Further the accuracy of Number Plate Extraction phase depends on the quality of captured vehicle image. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The existing methods of ANPR works well for dark and bright/light categories image but it does not work well for Low Contrast, Blurred and Noisy images and the detection of exact number plate area by using the existing ANPR approach is not successful even after applying existing filtering and enhancement technique for these types of images. Due to wrong extraction of number plate area, the character segmentation and character recognition are also not successful in this case by using the existing method. To overcome these drawbacks I proposed an efficient approach for ANPR in which the input vehicle image is pre-processed firstly by iterative bilateral filtering , adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, image binarization/thresholding, sobel vertical edge detection and by boundary box analysis. Sometimes the extracted plate area also contains noise, bolts, frames etc. So the extracted plate area is enhanced by using morphological operations to improve the quality of

  15. Hearing Status and Language Fluency as Predictors of Automatic Word and Sign Recognition.

    Science.gov (United States)

    Marschark, Marc; Shroyer, Edgar H.

    1993-01-01

    This study of the automatic word and sign recognition of 66 hearing and deaf adults found that responding in sign took longer and created more Stroop interference than responding orally, independent of hearing status. Deaf subjects showed greater automaticity in recognizing signs than words, whereas hearing subjects showed greater automaticity in…

  16. Voice Activity Detector of Wake-Up-Word Speech Recognition System Design on FPGA

    Directory of Open Access Journals (Sweden)

    Veton Z. Këpuska

    2014-12-01

    Full Text Available A typical speech recognition system is push-to-talk operated that requires activation. However for those who use hands-busy applications, movement may by restricted or impossible. One alternative is to use Speech-Only Interface. The proposed method that is called Wake-Up-Word Speech Recognition (WUW-SR that utilizes speech only interface. A WUW-SR system would allow the user to activate systems (Cell phone, Computer, etc. with only speech commands instead of manual activation. The trend in WUW-SR hardware design is towards implementing a complete system on a single chip intended for various applications. This paper presents an experimental FPGA design and implementation of a novel architecture of a real time feature extraction processor that includes: Voice Activity Detector (VAD, and features extraction, MFCC, LPC, and ENH_MFCC. In the WUW-SR system, the recognizer front-end with VAD is located at the terminal which is typically connected over a data network(e.g., serverfor remote back-end recognition. VAD is responsible for segmenting the signal into speech-like and non-speech-like segments. For any given frame VAD reports one of two possible states: VAD_ON or VAD_OFF. The back-end is then responsible to score the features that are being segmented during VAD_ON stage. The most important characteristic of the presented design is that it should guarantee virtually 100% correct rejection for non-WUW (out of vocabulary words - OOV while maintaining correct acceptance rate of 99.9% or higher (in vocabulary words - INV. This requirement sets apart WUW-SR from other speech recognition tasks because no existing system can guarantee 100% reliability by any measure.

  17. Using the FASST source separation toolbox for noise robust speech recognition

    OpenAIRE

    Ozerov, Alexey; Vincent, Emmanuel

    2011-01-01

    We describe our submission to the 2011 CHiME Speech Separation and Recognition Challenge. Our speech separation algorithm was built using the Flexible Audio Source Separation Toolbox (FASST) we developed recently. This toolbox is an implementation of a general flexible framework based on a library of structured source models that enable the incorporation of prior knowledge about a source separation problem via user-specifiable constraints. We show how to use FASST to develop an efficient spee...

  18. Chinese Speech Recognition Model Based on Activation of the State Feedback Neural Network

    Institute of Scientific and Technical Information of China (English)

    李先志; 孙义和

    2001-01-01

    This paper proposes a simplified novel speech recognition model, the state feedback neuralnetwork activation model (SFNNAM), which is developed based on the characteristics of Chinese speechstructure. The model assumes that the current state of speech is only a correction of the last previous state.According to the "C-V"(Consonant-Vowel) structure of the Chinese language, a speech segmentation methodis also implemented in the SFNNAM model. This model has a definite physical meaning grounded on thestructure of the Chinese language and is easily implemented in very large scale integrated circuit (VLSI). In thespeech recognition experiment, less calculations were need than in the hidden Markov models (HMM) basedalgorithm. The recognition rate for Chinese numbers was 93.5% for the first candidate and 99.5% for the firsttwo candidates.``

  19. [Research on Barrier-free Home Environment System Based on Speech Recognition].

    Science.gov (United States)

    Zhu, Husheng; Yu, Hongliu; Shi, Ping; Fang, Youfang; Jian, Zhuo

    2015-10-01

    The number of people with physical disabilities is increasing year by year, and the trend of population aging is more and more serious. In order to improve the quality of the life, a control system of accessible home environment for the patients with serious disabilities was developed to control the home electrical devices with the voice of the patients. The control system includes a central control platform, a speech recognition module, a terminal operation module, etc. The system combines the speech recognition control technology and wireless information transmission technology with the embedded mobile computing technology, and interconnects the lamp, electronic locks, alarms, TV and other electrical devices in the home environment as a whole system through a wireless network node. The experimental results showed that speech recognition success rate was more than 84% in the home environment. PMID:26964305

  20. Hybrid Approach for Language Identification Oriented to Multilingual Speech Recognition in the Basque Context

    Science.gov (United States)

    Barroso, N.; de Ipiña, K. López; Ezeiza, A.; Barroso, O.; Susperregi, U.

    The development of Multilingual Large Vocabulary Continuous Speech Recognition systems involves issues as: Language Identification, Acoustic-Phonetic Decoding, Language Modelling or the development of appropriated Language Resources. The interest on Multilingual Systems arouses because there are three official languages in the Basque Country (Basque, Spanish, and French), and there is much linguistic interaction among them, even if Basque has very different roots than the other two languages. This paper describes the development of a Language Identification (LID) system oriented to robust Multilingual Speech Recognition for the Basque context. The work presents hybrid strategies for LID, based on the selection of system elements by Support Vector Machines and Multilayer Perceptron classifiers and stochastic methods for speech recognition tasks (Hidden Markov Models and n-grams).

  1. Health Care in Home Automation Systems with Speech Recognition and Mobile Technology

    Directory of Open Access Journals (Sweden)

    Jasmin Kurti

    2016-08-01

    Full Text Available - Home automation systems use technology to facilitate the lives of people using it, and it is especially useful for assisting the elderly and persons with special needs. These kind of systems have been a popular research subject in last few years. In this work, I present the design and development of a system that provides a life assistant service in a home environment, a smart home-based healthcare system controlled with speech recognition and mobile technology. This includes developing software with speech recognition, speech synthesis, face recognition, controls for Arduino hardware, and a smartphone application for remote controlling the system. With the developed system, elderly and persons with special needs can stay independently in their own home secure and with care facilities. This system is tailored towards the elderly and disabled, but it can also be embedded in any home and used by anybody. It provides healthcare, security, entertainment, and total local and remote control of home.

  2. Low-cost speech recognition system for small vocabulary and independent speaker

    Science.gov (United States)

    Teh, Chih Chiang; Jong, Ching C.; Siek, Liter

    2000-10-01

    In this paper an ASIC implementation of a low cost speech recognition system for small vocabulary, 15 isolated word, speaker independent is presented. The IC is a digital block that receives a 12 bit sample with a sampling rate of 11.025 kHz as its input. The IC is running at 10 MHz system clock and targeted at 0.35 micrometers CMOS process. The whole chip, which includes the speech recognition system core, RAM and ROM contains about 61000 gates. The die size is 1.5 mm by 3 mm. The current design had been coded in VHDL for hardware implementation and its functionality is identical with the Matlab simulation. The average speech recognition rate for this IC is 89 percent for 15 isolated words.

  3. Frequency band-importance functions for auditory and auditory-visual speech recognition

    Science.gov (United States)

    Grant, Ken W.

    2005-04-01

    In many everyday listening environments, speech communication involves the integration of both acoustic and visual speech cues. This is especially true in noisy and reverberant environments where the speech signal is highly degraded, or when the listener has a hearing impairment. Understanding the mechanisms involved in auditory-visual integration is a primary interest of this work. Of particular interest is whether listeners are able to allocate their attention to various frequency regions of the speech signal differently under auditory-visual conditions and auditory-alone conditions. For auditory speech recognition, the most important frequency regions tend to be around 1500-3000 Hz, corresponding roughly to important acoustic cues for place of articulation. The purpose of this study is to determine the most important frequency region under auditory-visual speech conditions. Frequency band-importance functions for auditory and auditory-visual conditions were obtained by having subjects identify speech tokens under conditions where the speech-to-noise ratio of different parts of the speech spectrum is independently and randomly varied on every trial. Point biserial correlations were computed for each separate spectral region and the normalized correlations are interpreted as weights indicating the importance of each region. Relations among frequency-importance functions for auditory and auditory-visual conditions will be discussed.

  4. Self-organizing map classifier for stressed speech recognition

    Science.gov (United States)

    Partila, Pavol; Tovarek, Jaromir; Voznak, Miroslav

    2016-05-01

    This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility.

  5. Sign language perception research for improving automatic sign language recognition

    Science.gov (United States)

    ten Holt, Gineke A.; Arendsen, Jeroen; de Ridder, Huib; Koenderink-van Doorn, Andrea J.; Reinders, Marcel J. T.; Hendriks, Emile A.

    2009-02-01

    Current automatic sign language recognition (ASLR) seldom uses perceptual knowledge about the recognition of sign language. Using such knowledge can improve ASLR because it can give an indication which elements or phases of a sign are important for its meaning. Also, the current generation of data-driven ASLR methods has shortcomings which may not be solvable without the use of knowledge on human sign language processing. Handling variation in the precise execution of signs is an example of such shortcomings: data-driven methods (which include almost all current methods) have difficulty recognizing signs that deviate too much from the examples that were used to train the method. Insight into human sign processing is needed to solve these problems. Perceptual research on sign language can provide such insights. This paper discusses knowledge derived from a set of sign perception experiments, and the application of such knowledge in ASLR. Among the findings are the facts that not all phases and elements of a sign are equally informative, that defining the 'correct' form for a sign is not trivial, and that statistical ASLR methods do not necessarily arrive at sign representations that resemble those of human beings. Apparently, current ASLR methods are quite different from human observers: their method of learning gives them different sign definitions, they regard each moment and element of a sign as equally important and they employ a single definition of 'correct' for all circumstances. If the object is for an ASLR method to handle natural sign language, then the insights from sign perception research must be integrated into ASLR.

  6. Studying the Speech Recognition Scores of Hearing Impaied Children by Using Nonesense Syllables

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Keyhani

    1998-09-01

    Full Text Available Background: The current article is aimed at evaluating speech recognition scores in hearing aid wearers to determine whether nonsense syllables are suitable speech materials to evaluate the effectiveness of their hearing aids. Method: Subjects were 60 children (15 males and 15 females with bilateral moderate and moderately severe sensorineural hearing impairment who were aged between 7.7-14 years old. Gain prescription was fitted by NAL method. Then speech evaluation was performed in a quiet place with and without hearing aid by using a list of 25 monosyllable words recorded on a tape. A list was prepared for the subjects to check in the correct response. The same method was used to obtain results for normal subjects. Results: The results revealed that the subjects using hearing aids achieved significantly higher SRS in comparison of not wearing it. Although the speech recognition ability was not compensated completely (the maximum score obtained was 60% it was also revealed that the syllable recognition ability in the less amplified frequencies were decreased. the SRS was very higher in normal subjects (with an average of 88%. Conclusion: It seems that Speech recognition score can prepare Audiologist with a more comprehensive method to evaluate the hearing aid benefits.

  7. Emotional recognition from the speech signal for a virtual education agent

    Science.gov (United States)

    Tickle, A.; Raghu, S.; Elshaw, M.

    2013-06-01

    This paper explores the extraction of features from the speech wave to perform intelligent emotion recognition. A feature extract tool (openSmile) was used to obtain a baseline set of 998 acoustic features from a set of emotional speech recordings from a microphone. The initial features were reduced to the most important ones so recognition of emotions using a supervised neural network could be performed. Given that the future use of virtual education agents lies with making the agents more interactive, developing agents with the capability to recognise and adapt to the emotional state of humans is an important step.

  8. Dynamic HMM Model with Estimated Dynamic Property in Continuous Mandarin Speech Recognition

    Institute of Scientific and Technical Information of China (English)

    CHENFeili; ZHUJie

    2003-01-01

    A new dynamic HMM (hiddem Markov model) has been introduced in this paper, which describes the relationship between dynamic property and feature of space. The method to estimate the dynamic property is discussed in this paper, which makes the dynamic HMMmuch more practical in real time speech recognition. Ex-periment on large vocabulary continuous Mandarin speech recognition task has shown that the dynamic HMM model can achieve about 10% of error reduction both for tonal and toneless syllable. Estimated dynamic property can achieve nearly same (even better) performance than using extracted dynamic property.

  9. Simultaneous Blind Separation and Recognition of Speech Mixtures Using Two Microphones to Control a Robot Cleaner

    OpenAIRE

    Heungkyu Lee

    2013-01-01

    This paper proposes a method for the simultaneous separation and recognition of speech mixtures in noisy environments using two‐channel based independent vector analysis (IVA) on a home‐robot cleaner. The issues to be considered in our target application are speech recognition at a distance and noise removal to cope with a variety of noises, including TV sounds, air conditioners, babble, and so on, that can occur in a house, where people can utter a voice command to control a robot cleaner at...

  10. Semi-automatic recognition of marine debris on beaches

    Science.gov (United States)

    Ge, Zhenpeng; Shi, Huahong; Mei, Xuefei; Dai, Zhijun; Li, Daoji

    2016-05-01

    An increasing amount of anthropogenic marine debris is pervading the earth’s environmental systems, resulting in an enormous threat to living organisms. Additionally, the large amount of marine debris around the world has been investigated mostly through tedious manual methods. Therefore, we propose the use of a new technique, light detection and ranging (LIDAR), for the semi-automatic recognition of marine debris on a beach because of its substantially more efficient role in comparison with other more laborious methods. Our results revealed that LIDAR should be used for the classification of marine debris into plastic, paper, cloth and metal. Additionally, we reconstructed a 3-dimensional model of different types of debris on a beach with a high validity of debris revivification using LIDAR-based individual separation. These findings demonstrate that the availability of this new technique enables detailed observations to be made of debris on a large beach that was previously not possible. It is strongly suggested that LIDAR could be implemented as an appropriate monitoring tool for marine debris by global researchers and governments.

  11. Automatic recognition of facial movement for paralyzed face.

    Science.gov (United States)

    Wang, Ting; Dong, Junyu; Sun, Xin; Zhang, Shu; Wang, Shengke

    2014-01-01

    Facial nerve paralysis is a common disease due to nerve damage. Most approaches for evaluating the degree of facial paralysis rely on a set of different facial movements as commanded by doctors. Therefore, automatic recognition of the patterns of facial movement is fundamental to the evaluation of the degree of facial paralysis. In this paper, a novel method named Active Shape Models plus Local Binary Patterns (ASMLBP) is presented for recognizing facial movement patterns. Firstly, the Active Shape Models (ASMs) are used in the method to locate facial key points. According to these points, the face is divided into eight local regions. Then the descriptors of these regions are extracted by using Local Binary Patterns (LBP) to recognize the patterns of facial movement. The proposed ASMLBP method is tested on both the collected facial paralysis database with 57 patients and another publicly available database named the Japanese Female Facial Expression (JAFFE). Experimental results demonstrate that the proposed method is efficient for both paralyzed and normal faces.

  12. Speech recognition in noise as a function of the number of spectral channels : Comparison of acoustic hearing and cochlear implants

    NARCIS (Netherlands)

    Friesen, LM; Shannon, RV; Baskent, D; Wang, YB

    2001-01-01

    Speech recognition was measured as a function of spectral resolution (number of spectral channels) and speech-to-noise ratio in normal-hearing (NH) and cochlear-implant (CI) listeners. Vowel, consonant, word, and sentence recognition were measured in five normal -hearing listeners, ten listeners wit

  13. Speech recognition in normal hearing and sensorineural hearing loss as a function of the number of spectral channels

    NARCIS (Netherlands)

    Baskent, Deniz

    2006-01-01

    Speech recognition by normal-hearing listeners improves as a function of the number of spectral channels when tested with a noiseband vocoder simulating cochlear implant signal processing. Speech recognition by the best cochlear implant users, however, saturates around eight channels and does not im

  14. Gaussian process classification using automatic relevance determination for SAR target recognition

    Science.gov (United States)

    Zhang, Xiangrong; Gou, Limin; Hou, Biao; Jiao, Licheng

    2010-10-01

    In this paper, a Synthetic Aperture Radar Automatic Target Recognition approach based on Gaussian process (GP) classification is proposed. It adopts kernel principal component analysis to extract sample features and implements target recognition by using GP classification with automatic relevance determination (ARD) function. Compared with k-Nearest Neighbor, Naïve Bayes classifier and Support Vector Machine, GP with ARD has the advantage of automatic model selection and hyper-parameter optimization. The experiments on UCI datasets and MSTAR database show that our algorithm is self-tuning and has better recognition accuracy as well.

  15. Speech recognition for the anaesthesia record during crisis scenarios

    DEFF Research Database (Denmark)

    Alapetite, Alexandre

    2008-01-01

    Introduction: This article describes the evaluation of a prototype speech-input interface to an anaesthesia patient record, conducted in a full-scale anaesthesia simulator involving six doctor-nurse anaesthetist teams. Objective: The aims of the experiment were, first, to assess the potential...... advantages and disadvantages of a vocal interface compared to the traditional touch-screen and keyboard interface to an electronic anaesthesia record during crisis situations; second, to assess the usability in a realistic work environment of some speech input strategies (hands-free vocal interface activated...... by a keyword; combination of command and free text modes); finally, to quantify some of the gains that could be provided by the speech input modality. Methods: Six anaesthesia teams composed of one doctor and one nurse were each confronted with two crisis scenarios in a full-scale anaesthesia simulator. Each...

  16. Speech Emotion Recognition Based on Parametric Filter and Fractal Dimension

    Science.gov (United States)

    Mao, Xia; Chen, Lijiang

    In this paper, we propose a new method that employs two novel features, correlation density (Cd) and fractal dimension (Fd), to recognize emotional states contained in speech. The former feature obtained by a list of parametric filters reflects the broad frequency components and the fine structure of lower frequency components, contributed by unvoiced phones and voiced phones, respectively; the latter feature indicates the non-linearity and self-similarity of a speech signal. Comparative experiments based on Hidden Markov Model and K Nearest Neighbor methods are carried out. The results show that Cd and Fd are much more closely related with emotional expression than the features commonly used.

  17. Space discriminative function for microphone array robust speech recognition

    Institute of Scientific and Technical Information of China (English)

    Zhao Xianyu; Ou Zhijian; Wang Zuoying

    2005-01-01

    Based on W-disjoint orthogonality of speech mixtures, a space discriminative function was proposed to enumerate and localize competing speakers in the surrounding environments. Then, a Wiener-like post-filterer was developed to adaptively suppress interferences. Experimental results with a hands-free speech recognizer under various SNR and competing speakers settings show that nearly 69% error reduction can be obtained with a two-channel small aperture microphone array against the conventional single microphone baseline system. Comparisons were made against traditional delay-and-sum and Griffiths-Jim adaptive beamforming techniques to further assess the effectiveness of this method.

  18. AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    JIDE JULIUS POPOOLA

    2014-04-01

    Full Text Available In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the problem of how to accurately and promptly recognize a modulation format. In addressing these challenges, the development of automatic modulation recognition systems that can classify a radio signal’s modulation format has received worldwide attention. Decision-theoretic methods and pattern recognition solutions are the two typical automatic modulation recognition approaches. While decision-theoretic approaches use probabilistic or likelihood functions, pattern recognition uses feature-based methods. This study applies the pattern recognition approach based on statistical parameters, using an artificial neural network to classify five different digital modulation formats. The paper deals with automatic recognition of both inter-and intra-classes of digitally modulated signals in contrast to most of the existing algorithms in literature that deal with either inter-class or intra-class modulation format recognition. The results of this study show that accurate and prompt modulation recognition is possible beyond the lower bound of 5 dB commonly acclaimed in literature. The other significant contribution of this paper is the usage of the Python programming language which reduces computational complexity that characterizes other automatic modulation recognition classifiers developed using the conventional MATLAB neural network toolbox.

  19. Implementation of a Tour Guide Robot System Using RFID Technology and Viterbi Algorithm-Based HMM for Speech Recognition

    Directory of Open Access Journals (Sweden)

    Neng-Sheng Pai

    2014-01-01

    Full Text Available This paper applied speech recognition and RFID technologies to develop an omni-directional mobile robot into a robot with voice control and guide introduction functions. For speech recognition, the speech signals were captured by short-time processing. The speaker first recorded the isolated words for the robot to create speech database of specific speakers. After the speech pre-processing of this speech database, the feature parameters of cepstrum and delta-cepstrum were obtained using linear predictive coefficient (LPC. Then, the Hidden Markov Model (HMM was used for model training of the speech database, and the Viterbi algorithm was used to find an optimal state sequence as the reference sample for speech recognition. The trained reference model was put into the industrial computer on the robot platform, and the user entered the isolated words to be tested. After processing by the same reference model and comparing with previous reference model, the path of the maximum total probability in various models found using the Viterbi algorithm in the recognition was the recognition result. Finally, the speech recognition and RFID systems were achieved in an actual environment to prove its feasibility and stability, and implemented into the omni-directional mobile robot.

  20. Recognition of Rapid Speech by Blind and Sighted Older Adults

    Science.gov (United States)

    Gordon-Salant, Sandra; Friedman, Sarah A.

    2011-01-01

    Purpose: To determine whether older blind participants recognize time-compressed speech better than older sighted participants. Method: Three groups of adults with normal hearing participated (n = 10/group): (a) older sighted, (b) older blind, and (c) younger sighted listeners. Low-predictability sentences that were uncompressed (0% time…

  1. Temporal acuity and speech recognition score in noise in patients with multiple sclerosis

    Directory of Open Access Journals (Sweden)

    Mehri Maleki

    2014-04-01

    Full Text Available Background and Aim: Multiple sclerosis (MS is one of the central nervous system diseases can be associated with a variety of symptoms such as hearing disorders. The main consequence of hearing loss is poor speech perception, and temporal acuity has important role in speech perception. We evaluated the speech perception in silent and in the presence of noise and temporal acuity in patients with multiple sclerosis.Methods: Eighteen adults with multiple sclerosis with the mean age of 37.28 years and 18 age- and sex- matched controls with the mean age of 38.00 years participated in this study. Temporal acuity and speech perception were evaluated by random gap detection test (GDT and word recognition score (WRS in three different signal to noise ratios.Results: Statistical analysis of test results revealed significant differences between the two groups (p<0.05. Analysis of gap detection test (in 4 sensation levels and word recognition score in both groups showed significant differences (p<0.001.Conclusion: According to this survey, the ability of patients with multiple sclerosis to process temporal features of stimulus was impaired. It seems that, this impairment is important factor to decrease word recognition score and speech perception.

  2. ANALYSIS OF MULTIMODAL FUSION TECHNIQUES FOR AUDIO-VISUAL SPEECH RECOGNITION

    Directory of Open Access Journals (Sweden)

    D.V. Ivanko

    2016-05-01

    Full Text Available The paper deals with analytical review, covering the latest achievements in the field of audio-visual (AV fusion (integration of multimodal information. We discuss the main challenges and report on approaches to address them. One of the most important tasks of the AV integration is to understand how the modalities interact and influence each other. The paper addresses this problem in the context of AV speech processing and speech recognition. In the first part of the review we set out the basic principles of AV speech recognition and give the classification of audio and visual features of speech. Special attention is paid to the systematization of the existing techniques and the AV data fusion methods. In the second part we provide a consolidated list of tasks and applications that use the AV fusion based on carried out analysis of research area. We also indicate used methods, techniques, audio and video features. We propose classification of the AV integration, and discuss the advantages and disadvantages of different approaches. We draw conclusions and offer our assessment of the future in the field of AV fusion. In the further research we plan to implement a system of audio-visual Russian continuous speech recognition using advanced methods of multimodal fusion.

  3. Acoustic Feature Optimization Based on F-Ratio for Robust Speech Recognition

    Science.gov (United States)

    Sun, Yanqing; Zhou, Yu; Zhao, Qingwei; Yan, Yonghong

    This paper focuses on the problem of performance degradation in mismatched speech recognition. The F-Ratio analysis method is utilized to analyze the significance of different frequency bands for speech unit classification, and we find that frequencies around 1kHz and 3kHz, which are the upper bounds of the first and the second formants for most of the vowels, should be emphasized in comparison to the Mel-frequency cepstral coefficients (MFCC). The analysis result is further observed to be stable in several typical mismatched situations. Similar to the Mel-Frequency scale, another frequency scale called the F-Ratio-scale is thus proposed to optimize the filter bank design for the MFCC features, and make each subband contains equal significance for speech unit classification. Under comparable conditions, with the modified features we get a relative 43.20% decrease compared with the MFCC in sentence error rate for the emotion affected speech recognition, 35.54%, 23.03% for the noisy speech recognition at 15dB and 0dB SNR (signal to noise ratio) respectively, and 64.50% for the three years' 863 test data. The application of the F-Ratio analysis on the clean training set of the Aurora2 database demonstrates its robustness over languages, texts and sampling rates.

  4. Audiovisual cues benefit recognition of accented speech in noise but not perceptual adaptation.

    Science.gov (United States)

    Banks, Briony; Gowen, Emma; Munro, Kevin J; Adank, Patti

    2015-01-01

    Perceptual adaptation allows humans to recognize different varieties of accented speech. We investigated whether perceptual adaptation to accented speech is facilitated if listeners can see a speaker's facial and mouth movements. In Study 1, participants listened to sentences in a novel accent and underwent a period of training with audiovisual or audio-only speech cues, presented in quiet or in background noise. A control group also underwent training with visual-only (speech-reading) cues. We observed no significant difference in perceptual adaptation between any of the groups. To address a number of remaining questions, we carried out a second study using a different accent, speaker and experimental design, in which participants listened to sentences in a non-native (Japanese) accent with audiovisual or audio-only cues, without separate training. Participants' eye gaze was recorded to verify that they looked at the speaker's face during audiovisual trials. Recognition accuracy was significantly better for audiovisual than for audio-only stimuli; however, no statistical difference in perceptual adaptation was observed between the two modalities. Furthermore, Bayesian analysis suggested that the data supported the null hypothesis. Our results suggest that although the availability of visual speech cues may be immediately beneficial for recognition of unfamiliar accented speech in noise, it does not improve perceptual adaptation.

  5. Comparative Evaluation of Three Continuous Speech Recognition Software Packages in the Generation of Medical Reports

    OpenAIRE

    Devine, Eric G.; Gaehde, Stephan A.; Curtis, Arthur C.

    2000-01-01

    Objective: To compare out-of-box performance of three commercially available continuous speech recognition software packages: IBM ViaVoice 98 with General Medicine Vocabulary; Dragon Systems NaturallySpeaking Medical Suite, version 3.0; and L&H Voice Xpress for Medicine, General Medicine Edition, version 1.2.

  6. Phonotactics Constraints and the Spoken Word Recognition of Chinese Words in Speech

    Science.gov (United States)

    Yip, Michael C.

    2016-01-01

    Two word-spotting experiments were conducted to examine the question of whether native Cantonese listeners are constrained by phonotactics information in spoken word recognition of Chinese words in speech. Because no legal consonant clusters occurred within an individual Chinese word, this kind of categorical phonotactics information of Chinese…

  7. Review of Speech-to-Text Recognition Technology for Enhancing Learning

    Science.gov (United States)

    Shadiev, Rustam; Hwang, Wu-Yuin; Chen, Nian-Shing; Huang, Yueh-Min

    2014-01-01

    This paper reviewed literature from 1999 to 2014 inclusively on how Speech-to-Text Recognition (STR) technology has been applied to enhance learning. The first aim of this review is to understand how STR technology has been used to support learning over the past fifteen years, and the second is to analyze all research evidence to understand how…

  8. The Affordance of Speech Recognition Technology for EFL Learning in an Elementary School Setting

    Science.gov (United States)

    Liaw, Meei-Ling

    2014-01-01

    This study examined the use of speech recognition (SR) technology to support a group of elementary school children's learning of English as a foreign language (EFL). SR technology has been used in various language learning contexts. Its application to EFL teaching and learning is still relatively recent, but a solid understanding of its…

  9. Recognition of temporally interrupted and spectrally degraded sentences with additional unprocessed low-frequency speech

    NARCIS (Netherlands)

    Baskent, Deniz; Chatterjeec, Monita

    2010-01-01

    Recognition of periodically interrupted sentences (with an interruption rate of 1.5 Hz, 50% duty cycle) was investigated under conditions of spectral degradation, implemented with a noiseband vocoder, with and without additional unprocessed low-pass filtered speech (cutoff frequency 500 Hz). Intelli

  10. Variable Frame Rate and Length Analysis for Data Compression in Distributed Speech Recognition

    DEFF Research Database (Denmark)

    Kraljevski, Ivan; Tan, Zheng-Hua

    2014-01-01

    This paper addresses the issue of data compression in distributed speech recognition on the basis of a variable frame rate and length analysis method. The method first conducts frame selection by using a posteriori signal-to-noise ratio weighted energy distance to find the right time resolution...

  11. Automatic evaluation of speech rhythm instability and acceleration in dysarthrias associated with basal ganglia dysfunction

    Directory of Open Access Journals (Sweden)

    Jan eRusz

    2015-07-01

    Full Text Available Speech rhythm abnormalities are commonly present in patients with different neurodegenerative disorders. These alterations are hypothesized to be a consequence of disruption to the basal ganglia circuitry involving dysfunction of motor planning, programming and execution, which can be detected by a syllable repetition paradigm. Therefore, the aim of the present study was to design a robust signal processing technique that allows the automatic detection of spectrally-distinctive nuclei of syllable vocalizations and to determine speech features that represent rhythm instability and acceleration. A further aim was to elucidate specific patterns of dysrhythmia across various neurodegenerative disorders that share disruption of basal ganglia function. Speech samples based on repetition of the syllable /pa/ at a self-determined steady pace were acquired from 109 subjects, including 22 with Parkinson's disease (PD, 11 progressive supranuclear palsy (PSP, 9 multiple system atrophy (MSA, 24 ephedrone-induced parkinsonism (EP, 20 Huntington's disease (HD, and 23 healthy controls. Subsequently, an algorithm for the automatic detection of syllables as well as features representing rhythm instability and rhythm acceleration were designed. The proposed detection algorithm was able to correctly identify syllables and remove erroneous detections due to excessive inspiration and nonspeech sounds with a very high accuracy of 99.6%. Instability of vocal pace performance was observed in PSP, MSA, EP and HD groups. Significantly increased pace acceleration was observed only in the PD group. Although not significant, a tendency for pace acceleration was observed also in the PSP and MSA groups. Our findings underline the crucial role of the basal ganglia in the execution and maintenance of automatic speech motor sequences. We envisage the current approach to become the first step towards the development of acoustic technologies allowing automated assessment of rhythm

  12. Integrating Stress Information in Large Vocabulary Continuous Speech Recognition

    OpenAIRE

    Ludusan, Bogdan; Ziegler, Stefan; Gravier, Guillaume

    2012-01-01

    In this paper we propose a novel method for integrating stress information in the decoding step of a speech recognizer. A multiscale rhythm model was used to determine the stress scores for each syllable, which are further used to reinforce paths during search. Two strategies for integrating the stress were employed: the first one reinforces paths through all the syllables with a value proportional to the their stress score, while the second one enhances paths passing only through stressed sy...

  13. Iconic Gestures for Robot Avatars, Recognition and Integration with Speech.

    Science.gov (United States)

    Bremner, Paul; Leonards, Ute

    2016-01-01

    Co-verbal gestures are an important part of human communication, improving its efficiency and efficacy for information conveyance. One possible means by which such multi-modal communication might be realized remotely is through the use of a tele-operated humanoid robot avatar. Such avatars have been previously shown to enhance social presence and operator salience. We present a motion tracking based tele-operation system for the NAO robot platform that allows direct transmission of speech and gestures produced by the operator. To assess the capabilities of this system for transmitting multi-modal communication, we have conducted a user study that investigated if robot-produced iconic gestures are comprehensible, and are integrated with speech. Robot performed gesture outcomes were compared directly to those for gestures produced by a human actor, using a within participant experimental design. We show that iconic gestures produced by a tele-operated robot are understood by participants when presented alone, almost as well as when produced by a human. More importantly, we show that gestures are integrated with speech when presented as part of a multi-modal communication equally well for human and robot performances.

  14. Iconic Gestures for Robot Avatars, Recognition and Integration with Speech.

    Science.gov (United States)

    Bremner, Paul; Leonards, Ute

    2016-01-01

    Co-verbal gestures are an important part of human communication, improving its efficiency and efficacy for information conveyance. One possible means by which such multi-modal communication might be realized remotely is through the use of a tele-operated humanoid robot avatar. Such avatars have been previously shown to enhance social presence and operator salience. We present a motion tracking based tele-operation system for the NAO robot platform that allows direct transmission of speech and gestures produced by the operator. To assess the capabilities of this system for transmitting multi-modal communication, we have conducted a user study that investigated if robot-produced iconic gestures are comprehensible, and are integrated with speech. Robot performed gesture outcomes were compared directly to those for gestures produced by a human actor, using a within participant experimental design. We show that iconic gestures produced by a tele-operated robot are understood by participants when presented alone, almost as well as when produced by a human. More importantly, we show that gestures are integrated with speech when presented as part of a multi-modal communication equally well for human and robot performances. PMID:26925010

  15. Iconic Gestures for Robot Avatars, Recognition and Integration with Speech

    Directory of Open Access Journals (Sweden)

    Paul Adam Bremner

    2016-02-01

    Full Text Available Co-verbal gestures are an important part of human communication, improving its efficiency and efficacy for information conveyance. One possible means by which such multi-modal communication might be realised remotely is through the use of a tele-operated humanoid robot avatar. Such avatars have been previously shown to enhance social presence and operator salience. We present a motion tracking based tele-operation system for the NAO robot platform that allows direct transmission of speech and gestures produced by the operator. To assess the capabilities of this system for transmitting multi-modal communication, we have conducted a user study that investigated if robot-produced iconic gestures are comprehensible, and are integrated with speech. Robot performed gesture outcomes were compared directly to those for gestures produced by a human actor, using a within participant experimental design. We show that iconic gestures produced by a tele-operated robot are understood by participants when presented alone, almost as well as when produced by a human. More importantly, we show that gestures are integrated with speech when presented as part of a multi-modal communication equally well for human and robot performances.

  16. Audio-Visual Tibetan Speech Recognition Based on a Deep Dynamic Bayesian Network for Natural Human Robot Interaction

    Directory of Open Access Journals (Sweden)

    Yue Zhao

    2012-12-01

    Full Text Available Audio‐visual speech recognition is a natural and robust approach to improving human-robot interaction in noisy environments. Although multi‐stream Dynamic Bayesian Network and coupled HMM are widely used for audio‐visual speech recognition, they fail to learn the shared features between modalities and ignore the dependency of features among the frames within each discrete state. In this paper, we propose a Deep Dynamic Bayesian Network (DDBN to perform unsupervised extraction of spatial‐temporal multimodal features from Tibetan audio‐visual speech data and build an accurate audio‐visual speech recognition model under a no frame‐independency assumption. The experiment results on Tibetan speech data from some real‐world environments showed the proposed DDBN outperforms the state‐of‐art methods in word recognition accuracy.

  17. One-against-all weighted dynamic time warping for language-independent and speaker-dependent speech recognition in adverse conditions.

    Directory of Open Access Journals (Sweden)

    Xianglilan Zhang

    Full Text Available Considering personal privacy and difficulty of obtaining training material for many seldom used English words and (often non-English names, language-independent (LI with lightweight speaker-dependent (SD automatic speech recognition (ASR is a promising option to solve the problem. The dynamic time warping (DTW algorithm is the state-of-the-art algorithm for small foot-print SD ASR applications with limited storage space and small vocabulary, such as voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. Even though we have successfully developed two fast and accurate DTW variations for clean speech data, speech recognition for adverse conditions is still a big challenge. In order to improve recognition accuracy in noisy environment and bad recording conditions such as too high or low volume, we introduce a novel one-against-all weighted DTW (OAWDTW. This method defines a one-against-all index (OAI for each time frame of training data and applies the OAIs to the core DTW process. Given two speech signals, OAWDTW tunes their final alignment score by using OAI in the DTW process. Our method achieves better accuracies than DTW and merge-weighted DTW (MWDTW, as 6.97% relative reduction of error rate (RRER compared with DTW and 15.91% RRER compared with MWDTW are observed in our extensive experiments on one representative SD dataset of four speakers' recordings. To the best of our knowledge, OAWDTW approach is the first weighted DTW specially designed for speech data in adverse conditions.

  18. Automatic landmark detection and face recognition for side-view face images

    NARCIS (Netherlands)

    Santemiz, Pinar; Spreeuwers, Luuk J.; Veldhuis, Raymond N.J.; Broemme, Arslan; Busch, Christoph

    2013-01-01

    In real-life scenarios where pose variation is up to side-view positions, face recognition becomes a challenging task. In this paper we propose an automatic side-view face recognition system designed for home-safety applications. Our goal is to recognize people as they pass through doors in order to

  19. Hybrid model decomposition of speech and noise in a radial basis function neural model framework

    DEFF Research Database (Denmark)

    Sørensen, Helge Bjarup Dissing; Hartmann, Uwe

    1994-01-01

    The aim of the paper is to focus on a new approach to automatic speech recognition in noisy environments where the noise has either stationary or non-stationary statistical characteristics. The aim is to perform automatic recognition of speech in the presence of additive car noise. The technique...

  20. Simultaneous Blind Separation and Recognition of Speech Mixtures Using Two Microphones to Control a Robot Cleaner

    Directory of Open Access Journals (Sweden)

    Heungkyu Lee

    2013-02-01

    Full Text Available This paper proposes a method for the simultaneous separation and recognition of speech mixtures in noisy environments using two‐channel based independent vector analysis (IVA on a home‐robot cleaner. The issues to be considered in our target application are speech recognition at a distance and noise removal to cope with a variety of noises, including TV sounds, air conditioners, babble, and so on, that can occur in a house, where people can utter a voice command to control a robot cleaner at any time and at any location, even while a robot cleaner is moving. Thus, the system should always be in a recognition‐ready state to promptly recognize a spoken word at any time, and the false acceptance rate should be lower. To cope with these issues, the keyword spotting technique is applied. In addition, a microphone alignment method and a model‐based real‐time IVA approach are proposed to effectively and simultaneously process the speech and noise sources, as well as to cover 360‐degree directions irrespective of distance. From the experimental evaluations, we show that the proposed method is robust in terms of speech recognition accuracy, even when the speaker location is unfixed and changes all the time. In addition, the proposed method shows good performance in severely noisy environments.

  1. Lip Localization and Viseme Classification for Visual Speech Recognition

    OpenAIRE

    Werda, Salah; Mahdi, Walid; Hamadou, Abdelmajid Ben

    2013-01-01

    The need for an automatic lip-reading system is ever increasing. Infact, today, extraction and reliable analysis of facial movements make up an important part in many multimedia systems such as videoconference, low communication systems, lip-reading systems. In addition, visual information is imperative among people with special needs. We can imagine, for example, a dependent person ordering a machine with an easy lip movement or by a simple syllable pronunciation. Moreover, people with heari...

  2. NIST and NFI-TNO evaluations of automatic speaker recognition

    NARCIS (Netherlands)

    Leeuwen, D.A. van; Martin, A.F.; Przybocki, M.A.; Bouten, J.S.

    2006-01-01

    In the past years, several text-independent speaker recognition evaluation campaigns have taken place. This paper reports on results of the NIST evaluation of 2004 and the NFI-TNO forensic speaker recognition evaluation held in 2003, and reflects on the history of the evaluation campaigns. The effec

  3. NIST and NFI-TNO evaluations of automatic speaker recognition

    NARCIS (Netherlands)

    Leeuwen, D.A. van; Martin, A.F.; Przybocki, M.A.; Bouten, J.S.

    2005-01-01

    In the past years, several text-independent speaker recognition evaluation campaigns have taken place. This paper reports on results of the NIST evaluation of 2004 and the NFI-TNO forensic speaker recognition evaluation held in 2003, and re.ects on the history of the evaluation campaigns. The e.ects

  4. Discriminative tonal feature extraction method in mandarin speech recognition

    Institute of Scientific and Technical Information of China (English)

    HUANG Hao; ZHU Jie

    2007-01-01

    To utilize the supra-segmental nature of Mandarin tones, this article proposes a feature extraction method for hidden markov model (HMM) based tone modeling. The method uses linear transforms to project F0 (fundamental frequency) features of neighboring syllables as compensations, and adds them to the original F0 features of the current syllable. The transforms are discriminatively trained by using an objective function termed as "minimum tone error", which is a smooth approximation of tone recognition accuracy. Experiments show that the new tonal features achieve 3.82% tone recognition rate improvement, compared with the baseline, using maximum likelihood trained HMM on the normal F0 features. Further experiments show that discriminative HMM training on the new features is 8.78% better than the baseline.

  5. A Factored Language Model for Prosody Dependent Speech Recognition

    OpenAIRE

    Chen, Ken; Hasegawa-Johnson, Mark A.; Cole, Jennifer S.

    2007-01-01

    In this chapter, we proposed a novel approach that improves the robustness of prosody dependent language modeling by leveraging the dependence between prosody and syntax. In our experiments on Radio News Corpus, a factorial prosody dependent language model estimated using our proposed approach has achieved as much as 31% reduction of the joint perplexity over a prosody dependent language model estimated using the standard Maximum Likelihood approach. In recognition experiments, our approach r...

  6. Study on Acoustic Modeling in a Mandarin Continuous Speech Recognition

    Institute of Scientific and Technical Information of China (English)

    PENG Di; LIU Gang; GUO Jun

    2007-01-01

    The design of acoustic models is of vital importance to build a reliable connection between acoustic waveform and linguistic messages in terms of individual speech units. According to the characteristic of Chinese phonemes,the base acoustic phoneme units set is decided and refined and a decision tree based state tying approach is explored.Since one of the advantages of top-down tying method is flexibility in maintaining a balance between model accuracy and complexity, relevant adjustments are conducted, such as the stopping criterion of decision tree node splitting, during which optimal thresholds are captured. Better results are achieved in improving acoustic modeling accuracy as well as minimizing the scale of the model to a trainable extent.

  7. The role of speech in the user interface : perspective and application

    OpenAIRE

    Abewusi, A.B.

    1994-01-01

    Consideration must be given to the implication of speech as a communication medium before deciding to use speech input or output in an interactive environment. There are several effective control strategies for improving the quality of speech. The utility of the speech has been demonstrated by application to several illustrative problems where their application has proved effective despite all the limitation of synthetic speech output and automatic speech recognition systems. (Résumé d'auteur)

  8. Robust multi-stream speech recognition based on weighting the output probabilities of feature components

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jun; WEI Gang; YU Hua; NING Genxin

    2009-01-01

    In the traditional multi-stream fusion methods of speech recognition, all the feature components in a data stream share the same stream weight, while their distortion levels are usually different when the speech recognizer works in noisy environments. To overcome this limitation of the traditional multi-stream frameworks, the current study proposes a new stream fusion method that weights not only the stream outputs, but also the output probabilities of feature components. How the stream and feature component weights in the new fusion method affect the decision is analyzed and two stream fusion schemes based on the 03iginalisation and soft decision models in the missing data techniques are proposed. Experimental results on the hybrid sub-band multi-stream speech recognizer show that the proposed schemes can adjust the stream influences on the decision adaptively and outperform the traditional multi-stream methods in various noisy environments.

  9. Low-Complexity Variable Frame Rate Analysis for Speech Recognition and Voice Activity Detection

    DEFF Research Database (Denmark)

    Tan, Zheng-Hua; Lindberg, Børge

    2010-01-01

    present a low-complexity and effective frame selection approach based on a posteriori signal-to-noise ratio (SNR) weighted energy distance: The use of an energy distance, instead of e.g. a standard cepstral distance, makes the approach computationally efficient and enables fine granularity search......, and the use of a posteriori SNR weighting emphasizes the reliable regions in noisy speech signals. It is experimentally found that the approach is able to assign a higher frame rate to fast changing events such as consonants, a lower frame rate to steady regions like vowels and no frames to silence, even...... for very low SNR signals. The resulting variable frame rate analysis method is applied to three speech processing tasks that are essential to natural interaction with intelligent environments. First, it is used for improving speech recognition performance in noisy environments. Secondly, the method is used...

  10. Coordinated control of an intelligent wheelchair based on a brain-computer interface and speech recognition

    Institute of Scientific and Technical Information of China (English)

    Hong-tao WANG; Yuan-qing LI; Tian-you YU

    2014-01-01

    An intelligent wheelchair is devised, which is controlled by a coordinated mechanism based on a brain-computer interface (BCI) and speech recognition. By performing appropriate activities, users can navigate the wheelchair with four steering behaviors (start, stop, turn left, and turn right). Five healthy subjects participated in an indoor experiment. The results demonstrate the efficiency of the coordinated control mechanism with satisfactory path and time optimality ratios, and show that speech recognition is a fast and accurate supplement for BCI-based control systems. The proposed intelligent wheelchair is especially suitable for patients suffering from paralysis (especially those with aphasia) who can learn to pronounce only a single sound (e.g.,‘ah’).

  11. Audio-Visual Speech Recognition Using Lip Information Extracted from Side-Face Images

    Directory of Open Access Journals (Sweden)

    Koji Iwano

    2007-03-01

    Full Text Available This paper proposes an audio-visual speech recognition method using lip information extracted from side-face images as an attempt to increase noise robustness in mobile environments. Our proposed method assumes that lip images can be captured using a small camera installed in a handset. Two different kinds of lip features, lip-contour geometric features and lip-motion velocity features, are used individually or jointly, in combination with audio features. Phoneme HMMs modeling the audio and visual features are built based on the multistream HMM technique. Experiments conducted using Japanese connected digit speech contaminated with white noise in various SNR conditions show effectiveness of the proposed method. Recognition accuracy is improved by using the visual information in all SNR conditions. These visual features were confirmed to be effective even when the audio HMM was adapted to noise by the MLLR method.

  12. A Log—Index Weighted Cepstral Distance Measure for Speech Recognition

    Institute of Scientific and Technical Information of China (English)

    郑方; 吴文虎; 等

    1997-01-01

    A log-index weighted cepstral distance measure is proposed and tested in speacker-independent and speaker-dependent isolated word recognition systems using statistic techniques.The weights for the cepstral coefficients of this measure equal the logarithm of the corresponding indices.The experimental results show that this kind of measure works better than any other weighted Euclidean cepstral distance measures on three speech databases.The error rate obtained using this measure is about 1.8 percent for three databases on average,which is a 25% reduction from that obtained using other measures,and a 40% reduction from that obtained using Log Likelihood Ratio(LLR)measure.The experimental results also show that this kind of distance measure woks well in both speaker-dependent and speaker-independent speech recognition systems.

  13. Tone model integration based on discriminative weight training for Putonghua speech recognition

    Institute of Scientific and Technical Information of China (English)

    HUANG Hao; ZHU Jie

    2008-01-01

    A discriminative framework of tone model integration in continuous speech recognition was proposed. The method uses model dependent weights to scale probabilities of the hidden Markov models based on spectral features and tone models based on tonal features.The weights are discriminatively trahined by minimum phone error criterion. Update equation of the model weights based on extended Baum-Welch algorithm is derived. Various schemes of model weight combination are evaluated and a smoothing technique is introduced to make training robust to over fitting. The proposed method is ewluated on tonal syllable output and character output speech recognition tasks. The experimental results show the proposed method has obtained 9.5% and 4.7% relative error reduction than global weight on the two tasks due to a better interpolation of the given models. This proves the effectiveness of discriminative trained model weights for tone model integration.

  14. Robust Features for Speech Recognition using Temporal Filtering Technique in the Presence of Impulsive Noise

    Directory of Open Access Journals (Sweden)

    Hajer Rahali

    2014-10-01

    Full Text Available In this paper we introduce a robust feature extractor, dubbed as Modified Function Cepstral Coefficients (MODFCC, based on gammachirp filterbank, Relative Spectral (RASTA and Autoregressive Moving-Average (ARMA filter. The goal of this work is to improve the robustness of speech recognition systems in additive noise and real-time reverberant environments. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC, RASTA and ARMA Frequency Cepstral Coefficients (RASTA-MFCC and ARMA-MFCC are the three main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC were tested and compared against the original RASTA-MFCC and ARMA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases.

  15. Combined Acoustic and Pronunciation Modelling for Non-Native Speech Recognition

    CERN Document Server

    Bouselmi, Ghazi; Illina, Irina

    2007-01-01

    In this paper, we present several adaptation methods for non-native speech recognition. We have tested pronunciation modelling, MLLR and MAP non-native pronunciation adaptation and HMM models retraining on the HIWIRE foreign accented English speech database. The ``phonetic confusion'' scheme we have developed consists in associating to each spoken phone several sequences of confused phones. In our experiments, we have used different combinations of acoustic models representing the canonical and the foreign pronunciations: spoken and native models, models adapted to the non-native accent with MAP and MLLR. The joint use of pronunciation modelling and acoustic adaptation led to further improvements in recognition accuracy. The best combination of the above mentioned techniques resulted in a relative word error reduction ranging from 46% to 71%.

  16. Audio-Visual Speech Recognition Using Lip Information Extracted from Side-Face Images

    Directory of Open Access Journals (Sweden)

    Iwano Koji

    2007-01-01

    Full Text Available This paper proposes an audio-visual speech recognition method using lip information extracted from side-face images as an attempt to increase noise robustness in mobile environments. Our proposed method assumes that lip images can be captured using a small camera installed in a handset. Two different kinds of lip features, lip-contour geometric features and lip-motion velocity features, are used individually or jointly, in combination with audio features. Phoneme HMMs modeling the audio and visual features are built based on the multistream HMM technique. Experiments conducted using Japanese connected digit speech contaminated with white noise in various SNR conditions show effectiveness of the proposed method. Recognition accuracy is improved by using the visual information in all SNR conditions. These visual features were confirmed to be effective even when the audio HMM was adapted to noise by the MLLR method.

  17. Performance Evaluation of Speech Recognition Systems as a Next-Generation Pilot-Vehicle Interface Technology

    Science.gov (United States)

    Arthur, Jarvis J., III; Shelton, Kevin J.; Prinzel, Lawrence J., III; Bailey, Randall E.

    2016-01-01

    During the flight trials known as Gulfstream-V Synthetic Vision Systems Integrated Technology Evaluation (GV-SITE), a Speech Recognition System (SRS) was used by the evaluation pilots. The SRS system was intended to be an intuitive interface for display control (rather than knobs, buttons, etc.). This paper describes the performance of the current "state of the art" Speech Recognition System (SRS). The commercially available technology was evaluated as an application for possible inclusion in commercial aircraft flight decks as a crew-to-vehicle interface. Specifically, the technology is to be used as an interface from aircrew to the onboard displays, controls, and flight management tasks. A flight test of a SRS as well as a laboratory test was conducted.

  18. Automatic model-based face reconstruction and recognition

    OpenAIRE

    Breuer, Pia

    2011-01-01

    Three-dimensional Morphable Models (3DMM) are known to be valuable tools for both face reconstruction and face recognition. These models are particularly relevant in safety applications or Computer Graphics. In this thesis, contributions are made to address the major difficulties preceding and during the fitting process of the Morphable Model in the framework of a fully automated system.It is shown to which extent the reconstruction and recognition results depend on the initialization and wha...

  19. Automatic Facial Expression Recognition Using Features of Salient Facial Patches

    OpenAIRE

    Happy, S L; Routray, Aurobinda

    2015-01-01

    Extraction of discriminative features from salient facial patches plays a vital role in effective facial expression recognition. The accurate detection of facial landmarks improves the localization of the salient patches on face images. This paper proposes a novel framework for expression recognition by using appearance features of selected facial patches. A few prominent facial patches, depending on the position of facial landmarks, are extracted which are active during emotion elicitation. ...

  20. A Computationally Efficient Mel-Filter Bank VAD Algorithm for Distributed Speech Recognition Systems

    Science.gov (United States)

    Vlaj, Damjan; Kotnik, Bojan; Horvat, Bogomir; Kačič, Zdravko

    2005-12-01

    This paper presents a novel computationally efficient voice activity detection (VAD) algorithm and emphasizes the importance of such algorithms in distributed speech recognition (DSR) systems. When using VAD algorithms in telecommunication systems, the required capacity of the speech transmission channel can be reduced if only the speech parts of the signal are transmitted. A similar objective can be adopted in DSR systems, where the nonspeech parameters are not sent over the transmission channel. A novel approach is proposed for VAD decisions based on mel-filter bank (MFB) outputs with the so-called Hangover criterion. Comparative tests are presented between the presented MFB VAD algorithm and three VAD algorithms used in the G.729, G.723.1, and DSR (advanced front-end) Standards. These tests were made on the Aurora 2 database, with different signal-to-noise (SNRs) ratios. In the speech recognition tests, the proposed MFB VAD outperformed all the three VAD algorithms used in the standards by [InlineEquation not available: see fulltext.] relative (G.723.1 VAD), by [InlineEquation not available: see fulltext.] relative (G.729 VAD), and by [InlineEquation not available: see fulltext.] relative (DSR VAD) in all SNRs.

  1. A Computationally Efficient Mel-Filter Bank VAD Algorithm for Distributed Speech Recognition Systems

    Directory of Open Access Journals (Sweden)

    Vlaj Damjan

    2005-01-01

    Full Text Available This paper presents a novel computationally efficient voice activity detection (VAD algorithm and emphasizes the importance of such algorithms in distributed speech recognition (DSR systems. When using VAD algorithms in telecommunication systems, the required capacity of the speech transmission channel can be reduced if only the speech parts of the signal are transmitted. A similar objective can be adopted in DSR systems, where the nonspeech parameters are not sent over the transmission channel. A novel approach is proposed for VAD decisions based on mel-filter bank (MFB outputs with the so-called Hangover criterion. Comparative tests are presented between the presented MFB VAD algorithm and three VAD algorithms used in the G.729, G.723.1, and DSR (advanced front-end Standards. These tests were made on the Aurora 2 database, with different signal-to-noise (SNRs ratios. In the speech recognition tests, the proposed MFB VAD outperformed all the three VAD algorithms used in the standards by relative (G.723.1 VAD, by relative (G.729 VAD, and by relative (DSR VAD in all SNRs.

  2. Managing predefined templates and macros for a departmental speech recognition system using common software

    OpenAIRE

    Sistrom, Chris L.; Honeyman, Janice C.; Mancuso, Anthony; Quisling, Ronald G.

    2001-01-01

    The authors have developed a networked database system to create, store, and manage predefined radiology report definitions. This was prompted by complete departmental conversion to a computer speech recognition system (SRS) for clinical reporting. The software complements and extends the capabilities of the SRS, and 2 systems are integrated by means of a simple text file format and import/export functions within each program. This report describes the functional requirements, design consider...

  3. Development of a Mandarin-English Bilingual Speech Recognition System for Real World Music Retrieval

    Science.gov (United States)

    Zhang, Qingqing; Pan, Jielin; Lin, Yang; Shao, Jian; Yan, Yonghong

    In recent decades, there has been a great deal of research into the problem of bilingual speech recognition-to develop a recognizer that can handle inter- and intra-sentential language switching between two languages. This paper presents our recent work on the development of a grammar-constrained, Mandarin-English bilingual Speech Recognition System (MESRS) for real world music retrieval. Two of the main difficult issues in handling the bilingual speech recognition systems for real world applications are tackled in this paper. One is to balance the performance and the complexity of the bilingual speech recognition system; the other is to effectively deal with the matrix language accents in embedded language**. In order to process the intra-sentential language switching and reduce the amount of data required to robustly estimate statistical models, a compact single set of bilingual acoustic models derived by phone set merging and clustering is developed instead of using two separate monolingual models for each language. In our study, a novel Two-pass phone clustering method based on Confusion Matrix (TCM) is presented and compared with the log-likelihood measure method. Experiments testify that TCM can achieve better performance. Since potential system users' native language is Mandarin which is regarded as a matrix language in our application, their pronunciations of English as the embedded language usually contain Mandarin accents. In order to deal with the matrix language accents in embedded language, different non-native adaptation approaches are investigated. Experiments show that model retraining method outperforms the other common adaptation methods such as Maximum A Posteriori (MAP). With the effective incorporation of approaches on phone clustering and non-native adaptation, the Phrase Error Rate (PER) of MESRS for English utterances was reduced by 24.47% relatively compared to the baseline monolingual English system while the PER on Mandarin utterances was

  4. Visual Word Recognition is Accompanied by Covert Articulation: Evidence for a Speech-like Phonological Representation

    OpenAIRE

    Eiter, Brianna M.; INHOFF, ALBRECHT W.

    2008-01-01

    Two lexical decision task (LDT) experiments examined whether visual word recognition involves the use of a speech-like phonological code that may be generated via covert articulation. In Experiment 1, each visual item was presented with an irrelevant spoken word (ISW) that was either phonologically identical, similar, or dissimilar to it. An ISW delayed classification of a visual word when the two were phonologically similar, and it delayed the classification of a pseudoword when it was ident...

  5. Audiovisual benefit for recognition of speech presented with single-talker noise in older listeners

    OpenAIRE

    Jesse, A.; Janse, E.

    2012-01-01

    Older listeners are more affected than younger listeners in their recognition of speech in adverse conditions, such as when they also hear a single-competing speaker. In the present study, we investigated with a speeded response task whether older listeners with various degrees of hearing loss benefit under such conditions from also seeing the speaker they intend to listen to. We also tested, at the same time, whether older adults need postperceptual processing to obtain an audiovisual benefi...

  6. Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

    Science.gov (United States)

    Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y.; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J.; Adatia, Ian

    2016-09-01

    We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral.

  7. A SPEECH RECOGNITION METHOD USING COMPETITIVE AND SELECTIVE LEARNING NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    On the basis of asymptotic theory of Gersho, the isodistortion principle of vector clustering was discussed and a kind of competitive and selective learning method (CSL) which may avoid local optimization and have excellent result in application to clusters of HMM model was also proposed. In combining the parallel, self-organizational hierarchical neural networks (PSHNN) to reclassify the scores of every form output by HMM, the CSL speech recognition rate is obviously elevated.

  8. Superior Speech Acquisition and Robust Automatic Speech Recognition for Integrated Spacesuit Audio Systems Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Astronauts suffer from poor dexterity of their hands due to the clumsy spacesuit gloves during Extravehicular Activity (EVA) operations and NASA has had a widely...

  9. Automatic recognition of lactating sow behaviors through depth image processing

    Science.gov (United States)

    Manual observation and classification of animal behaviors is laborious, time-consuming, and of limited ability to process large amount of data. A computer vision-based system was developed that automatically recognizes sow behaviors (lying, sitting, standing, kneeling, feeding, drinking, and shiftin...

  10. Automatic SIMD parallelization of embedded applications based on pattern recognition

    NARCIS (Netherlands)

    Manniesing, R.; Karkowski, I.P.; Corporaal, H.

    2000-01-01

    This paper investigates the potential for automatic mapping of typical embedded applications to architectures with multimedia instruction set extensions. For this purpose a (pattern matching based) code transformation engine is used, which involves a three-step process of matching, condition checkin

  11. Purging Musical Instrument Sample Databases Using Automatic Musical Instrument Recognition Methods

    OpenAIRE

    Livshin, Arie; Rodet, Xavier

    2009-01-01

    cote interne IRCAM: Livshin09a None / None National audience Compilation of musical instrument sample databases requires careful elimination of badly recorded samples and validation of sample classification into correct categories. This paper introduces algorithms for automatic removal of bad instrument samples using Automatic Musical Instrument Recognition and Outlier Detection techniques. Best evaluation results on a methodically contaminated sound database are achieved using the i...

  12. Automatic Signature Verification: Bridging the Gap between Existing Pattern Recognition Methods and Forensic Science

    OpenAIRE

    Malik, Muhammad Imran

    2015-01-01

    The main goal of this thesis is twofold. First, the thesis aims at bridging the gap between existing Pattern Recognition (PR) methods of automatic signature verification and the requirements for their application in forensic science. This gap, attributed by various factors ranging from system definition to evaluation, prevents automatic methods from being used by Forensic Handwriting Examiners (FHEs). Second, the thesis presents novel signature verification methods developed particularly cons...

  13. Automatic Eye Detection Error as a Predictor of Face Recognition Performance

    OpenAIRE

    Dutta, Abhishek; Veldhuis, Raymond; Spreeuwers, Luuk

    2014-01-01

    Various facial image quality parameters like pose, illumination, noise, resolution, etc are known to be a predictor of face recognition performance. However, there still remain many other properties of facial images that are not captured by the existing quality parameters. In this paper, we propose a novel image quality parameter called the Automatic Eye Detection Error (AEDE) which measures the difference between manually located and automatically detected eye coordinates. Our experiment res...

  14. Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks

    OpenAIRE

    Li, Peter; Qian, Jiyuan; Wang, Tian

    2015-01-01

    Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm. In these "shallow" architectures, feature engineering and learning are typically disjoint and unrelated. Additionally, feature engineering is difficult, and typically depends on extensive domain expertise. In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument ...

  15. Speech emotion recognition based on LS-SVM%基才LS-SVM的情感语音识别

    Institute of Scientific and Technical Information of China (English)

    周慧; 魏霖静

    2012-01-01

    The dissertation proposed an approach for emotional speech recognition based on LS-SVM. First, pitch frequency, energy, speech rate parameters extracted from speech signals as emotional features. Then emotional speech modeling is established with LS-SVM method. Experimental results show that, basic emotion recognition can get high recognition rates.%提出了一种基于LS—SVM的情感语音识别方法。即先提取实验中语音信号的基频,能量,语速等参数为情感特征,然后采用LS—SVM方法对相应的情感语音信号建立模型,进行识别。实验结果表明,利用LS—SVM进行基本情感识别时,识别率较高。

  16. The Effects of Noise on Speech Recognition in Cochlear Implant Subjects: Predictions and Analysis Using Acoustic Models

    Science.gov (United States)

    Remus, Jeremiah J.; Collins, Leslie M.

    2005-12-01

    Cochlear implants can provide partial restoration of hearing, even with limited spectral resolution and loss of fine temporal structure, to severely deafened individuals. Studies have indicated that background noise has significant deleterious effects on the speech recognition performance of cochlear implant patients. This study investigates the effects of noise on speech recognition using acoustic models of two cochlear implant speech processors and several predictive signal-processing-based analyses. The results of a listening test for vowel and consonant recognition in noise are presented and analyzed using the rate of phonemic feature transmission for each acoustic model. Three methods for predicting patterns of consonant and vowel confusion that are based on signal processing techniques calculating a quantitative difference between speech tokens are developed and tested using the listening test results. Results of the listening test and confusion predictions are discussed in terms of comparisons between acoustic models and confusion prediction performance.

  17. Development of an automated speech recognition interface for personal emergency response systems

    Directory of Open Access Journals (Sweden)

    Mihailidis Alex

    2009-07-01

    Full Text Available Abstract Background Demands on long-term-care facilities are predicted to increase at an unprecedented rate as the baby boomer generation reaches retirement age. Aging-in-place (i.e. aging at home is the desire of most seniors and is also a good option to reduce the burden on an over-stretched long-term-care system. Personal Emergency Response Systems (PERSs help enable older adults to age-in-place by providing them with immediate access to emergency assistance. Traditionally they operate with push-button activators that connect the occupant via speaker-phone to a live emergency call-centre operator. If occupants do not wear the push button or cannot access the button, then the system is useless in the event of a fall or emergency. Additionally, a false alarm or failure to check-in at a regular interval will trigger a connection to a live operator, which can be unwanted and intrusive to the occupant. This paper describes the development and testing of an automated, hands-free, dialogue-based PERS prototype. Methods The prototype system was built using a ceiling mounted microphone array, an open-source automatic speech recognition engine, and a 'yes' and 'no' response dialog modelled after an existing call-centre protocol. Testing compared a single microphone versus a microphone array with nine adults in both noisy and quiet conditions. Dialogue testing was completed with four adults. Results and discussion The microphone array demonstrated improvement over the single microphone. In all cases, dialog testing resulted in the system reaching the correct decision about the kind of assistance the user was requesting. Further testing is required with elderly voices and under different noise conditions to ensure the appropriateness of the technology. Future developments include integration of the system with an emergency detection method as well as communication enhancement using features such as barge-in capability. Conclusion The use of an automated

  18. Automatic target recognition in SAR images using multilinear analysis

    OpenAIRE

    Porgès, Tristan; Favier, Gérard

    2011-01-01

    International audience Multilinear analysis provides a powerful mathematical framework for analyzing synthetic aperture radar (SAR) images resulting from the interaction of multiple factors like sky luminosity and viewing angles, while preserving their original shape. In this paper, we propose a multilinear principal component analysis (MPCA) algorithm for target recognition in SAR images. First, we form a high order tensor with the training image set and we apply the higher-order singular...

  19. An Automatic Interference Recognition Method in Spread Spectrum Communication System

    Institute of Scientific and Technical Information of China (English)

    YANG Xiao-ming; TAO Ran

    2007-01-01

    An algorithm to detect and recognize interferences embedded in a direct sequence spread spectrum (DSSS) communication system is proposed. Based on Welch's averaging modified periodogram method and fractional Fourier transformation (FRFT), the paper proposes a decision tree-based algorithm in which a set of decision criteria for identifying different types of interferences is developed. Simulation results demonstrate that the proposed algorithm provides a high recognition rate and is robust for various ISR and SNR.

  20. Recognition of Speech of Normal-hearing Individuals with Tinnitus and Hyperacusis

    Directory of Open Access Journals (Sweden)

    Hennig, Tais Regina

    2011-01-01

    Full Text Available Introduction: Tinnitus and hyperacusis are increasingly frequent audiological symptoms that may occur in the absence of the hearing involvement, but it does not offer a lower impact or bothering to the affected individuals. The Medial Olivocochlear System helps in the speech recognition in noise and may be connected to the presence of tinnitus and hyperacusis. Objective: To evaluate the speech recognition of normal-hearing individual with and without complaints of tinnitus and hyperacusis, and to compare their results. Method: Descriptive, prospective and cross-study in which 19 normal-hearing individuals were evaluated with complaint of tinnitus and hyperacusis of the Study Group (SG, and 23 normal-hearing individuals without audiological complaints of the Control Group (CG. The individuals of both groups were submitted to the test List of Sentences in Portuguese, prepared by Costa (1998 to determine the Sentences Recognition Threshold in Silence (LRSS and the signal to noise ratio (S/N. The SG also answered the Tinnitus Handicap Inventory for tinnitus analysis, and to characterize hyperacusis the discomfort thresholds were set. Results: The CG and SG presented with average LRSS and S/N ratio of 7.34 dB NA and -6.77 dB, and of 7.20 dB NA and -4.89 dB, respectively. Conclusion: The normal-hearing individuals with or without audiological complaints of tinnitus and hyperacusis had a similar performance in the speech recognition in silence, which was not the case when evaluated in the presence of competitive noise, since the SG had a lower performance in this communication scenario, with a statistically significant difference.

  1. Automatic Recognition of Chinese Personal Name Using Conditional Random Fields and Knowledge Base

    Directory of Open Access Journals (Sweden)

    Chuan Gu

    2015-01-01

    Full Text Available According to the features of Chinese personal name, we present an approach for Chinese personal name recognition based on conditional random fields (CRF and knowledge base in this paper. The method builds multiple features of CRF model by adopting Chinese character as processing unit, selects useful features based on selection algorithm of knowledge base and incremental feature template, and finally implements the automatic recognition of Chinese personal name from Chinese document. The experimental results on open real corpus demonstrated the effectiveness of our method and obtained high accuracy rate and high recall rate of recognition.

  2. Automatic facial expression recognition based on features extracted from tracking of facial landmarks

    Science.gov (United States)

    Ghimire, Deepak; Lee, Joonwhoan

    2014-01-01

    In this paper, we present a fully automatic facial expression recognition system using support vector machines, with geometric features extracted from the tracking of facial landmarks. Facial landmark initialization and tracking is performed by using an elastic bunch graph matching algorithm. The facial expression recognition is performed based on the features extracted from the tracking of not only individual landmarks, but also pair of landmarks. The recognition accuracy on the Extended Kohn-Kanade (CK+) database shows that our proposed set of features produces better results, because it utilizes time-varying graph information, as well as the motion of individual facial landmarks.

  3. Automatic target recognition in synthetic aperture sonar images for autonomous mine hunting

    NARCIS (Netherlands)

    Quesson, B.A.J.; Sabel, J.C.; Bouma, H.; Dekker, R.J.; Lengrand-Lambert, J.

    2010-01-01

    The future of Mine Countermeasures (MCM) operations lies with unmanned platforms where Automatic Target Recognition (ATR) is an essential step in making the mine hunting process autonomous. At TNO, a new ATR method is currently being developed for use on an Autonomous Underwater Vehicle (AUV), using

  4. Modeling speech imitation and ecological learning of auditory-motor maps

    OpenAIRE

    Claudia eCanevari; Leonardo eBadino; Alessandro eD'Ausilio; Luciano eFadiga; Giorgio eMetta

    2013-01-01

    Classical models of speech consider an antero-posterior distinction between perceptive and productive functions. However, the selective alteration of neural activity in speech motor centers, via transcranial magnetic stimulation, was shown to affect speech discrimination. On the automatic speech recognition (ASR) side, the recognition systems have classically relied solely on acoustic data, achieving rather good performance in optimal listening conditions. The main limitations of current ASR ...

  5. Performance of Czech Speech Recognition with Language Models Created from Public Resources

    Directory of Open Access Journals (Sweden)

    V. Prochazka

    2011-12-01

    Full Text Available In this paper, we investigate the usability of publicly available n-gram corpora for the creation of language models (LM applicable for Czech speech recognition systems. N-gram LMs with various parameters and settings were created from two publicly available sets, Czech Web 1T 5-gram corpus provided by Google and 5-gram corpus obtained from the Czech National Corpus Institute. For comparison, we tested also an LM made of a large private resource of newspaper and broadcast texts collected by a Czech media mining company. The LMs were analyzed and compared from the statistic point of view (mainly via their perplexity rates and from the performance point of view when employed in large vocabulary continuous speech recognition systems. Our study shows that the Web1T-based LMs, even after intensive cleaning and normalization procedures, cannot compete with those made of smaller but more consistent corpora. The experiments done on large test data also illustrate the impact of Czech as highly inflective language on the perplexity, OOV, and recognition accuracy rates.

  6. Automatic music genres classification as a pattern recognition problem

    Science.gov (United States)

    Ul Haq, Ihtisham; Khan, Fauzia; Sharif, Sana; Shaukat, Arsalan

    2013-12-01

    Music genres are the simplest and effect descriptors for searching music libraries stores or catalogues. The paper compares the results of two automatic music genres classification systems implemented by using two different yet simple classifiers (K-Nearest Neighbor and Naïve Bayes). First a 10-12 second sample is selected and features are extracted from it, and then based on those features results of both classifiers are represented in the form of accuracy table and confusion matrix. An experiment carried out on test 60 taken from middle of a song represents the true essence of its genre as compared to the samples taken from beginning and ending of a song. The novel techniques have achieved an accuracy of 91% and 78% by using Naïve Bayes and KNN classifiers respectively.

  7. An Introduction to the Chinese Speech Recognition Front-End of the NICT/ATR Multi-Lingual Speech Translation System

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jinsong; Takatoshi Jitsuhiro; Hirofumi Yamamoto; HU Xinhui; Satoshi Nakamura

    2008-01-01

    This paper introduces several important features of the Chinese large vocabulary continuous speech recognition system in the NICT/ATR multi-lingual speech-to-speech translation system.The features include: (1) a flexible way to derive an information rich phoneme set based on mutual information between a text corpus and its phoneme set; (2) a hidden Markov network acoustic model and a successive state split-ting algorithm to generate its model topology based on a minimum description length criterion; and (3) ad-vanced language modeling using multi-class composite N-grams.These features allow a recognition per-formance of 90% character accuracy in tourism related dialogue with a real time response speed.

  8. INTEGRATING MACHINE TRANSLATION AND SPEECH SYNTHESIS COMPONENT FOR ENGLISH TO DRAVIDIAN LANGUAGE SPEECH TO SPEECH TRANSLATION SYSTEM

    Directory of Open Access Journals (Sweden)

    J. SANGEETHA

    2015-02-01

    Full Text Available This paper provides an interface between the machine translation and speech synthesis system for converting English speech to Tamil text in English to Tamil speech to speech translation system. The speech translation system consists of three modules: automatic speech recognition, machine translation and text to speech synthesis. Many procedures for incorporation of speech recognition and machine translation have been projected. Still speech synthesis system has not yet been measured. In this paper, we focus on integration of machine translation and speech synthesis, and report a subjective evaluation to investigate the impact of speech synthesis, machine translation and the integration of machine translation and speech synthesis components. Here we implement a hybrid machine translation (combination of rule based and statistical machine translation and concatenative syllable based speech synthesis technique. In order to retain the naturalness and intelligibility of synthesized speech Auto Associative Neural Network (AANN prosody prediction is used in this work. The results of this system investigation demonstrate that the naturalness and intelligibility of the synthesized speech are strongly influenced by the fluency and correctness of the translated text.

  9. Current trends in multilingual speech processing

    Indian Academy of Sciences (India)

    Hervé Bourlard; John Dines; Mathew Magimai-Doss; Philip N Garner; David Imseng; Petr Motlicek; Hui Liang; Lakshmi Saheer; Fabio Valente

    2011-10-01

    In this paper, we describe recent work at Idiap Research Institute in the domain of multilingual speech processing and provide some insights into emerging challenges for the research community. Multilingual speech processing has been a topic of ongoing interest to the research community for many years and the field is now receiving renewed interest owing to two strong driving forces. Firstly, technical advances in speech recognition and synthesis are posing new challenges and opportunities to researchers. For example, discriminative features are seeing wide application by the speech recognition community, but additional issues arise when using such features in a multilingual setting. Another example is the apparent convergence of speech recognition and speech synthesis technologies in the form of statistical parametric methodologies. This convergence enables the investigation of new approaches to unified modelling for automatic speech recognition and text-to-speech synthesis (TTS) as well as cross-lingual speaker adaptation for TTS. The second driving force is the impetus being provided by both government and industry for technologies to help break down domestic and international language barriers, these also being barriers to the expansion of policy and commerce. Speech-to-speech and speech-to-text translation are thus emerging as key technologies at the heart of which lies multilingual speech processing.

  10. Cued Speech: A visual communication mode for the Deaf society

    OpenAIRE

    Heracleous, Panikos; Beautemps, Denis

    2010-01-01

    Cued Speech is a visual mode of communication that uses handshapes and placements in combination with the mouth movements of speech to make the phonemes of a spoken language look different from each other and clearly understandable to deaf individuals. The aim of Cued Speech is to overcome the problems of lip reading and thus enable deaf persons to wholly understand spoken language. In this study, automatic phoneme recognition in Cued Speech for French based on hidden Markov model (HMMs) is i...

  11. EEG Responses to Auditory Stimuli for Automatic Affect Recognition

    Science.gov (United States)

    Hettich, Dirk T.; Bolinger, Elaina; Matuz, Tamara; Birbaumer, Niels; Rosenstiel, Wolfgang; Spüler, Martin

    2016-01-01

    Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has gained increased attention in the interdisciplinary field of affective computing. We investigated how well specific emotional reactions, induced by auditory stimuli, can be detected in EEG recordings. We introduce an auditory emotion induction paradigm based on the International Affective Digitized Sounds 2nd Edition (IADS-2) database also suitable for disabled individuals. Stimuli are grouped in three valence categories: unpleasant, neutral, and pleasant. Significant differences in time domain domain event-related potentials are found in the electroencephalogram (EEG) between unpleasant and neutral, as well as pleasant and neutral conditions over midline electrodes. Time domain data were classified in three binary classification problems using a linear support vector machine (SVM) classifier. We discuss three classification performance measures in the context of affective computing and outline some strategies for conducting and reporting affect classification studies. PMID:27375410

  12. A Novel Neural Network Based Method Developed for Digit Recognition Applied to Automatic Speed Sign Recognition

    Directory of Open Access Journals (Sweden)

    Hanene Rouabeh

    2016-02-01

    Full Text Available This Paper presents a new hybrid technique for digit recognition applied to the speed limit sign recognition task. The complete recognition system consists in the detection and recognition of the speed signs in RGB images. A pretreatment is applied to extract the pictogram from a detected circular road sign, and then the task discussed in this work is employed to recognize digit candidates. To realize a compromise between performances, reduced execution time and optimized memory resources, the developed method is based on a conjoint use of a Neural Network and a Decision Tree. A simple Network is employed firstly to classify the extracted candidates into three classes and secondly a small Decision Tree is charged to determine the exact information. This combination is used to reduce the size of the Network as well as the memory resources utilization. The evaluation of the technique and the comparison with existent methods show the effectiveness.

  13. High-order hidden Markov model for piecewise linear processes and applications to speech recognition.

    Science.gov (United States)

    Lee, Lee-Min; Jean, Fu-Rong

    2016-08-01

    The hidden Markov models have been widely applied to systems with sequential data. However, the conditional independence of the state outputs will limit the output of a hidden Markov model to be a piecewise constant random sequence, which is not a good approximation for many real processes. In this paper, a high-order hidden Markov model for piecewise linear processes is proposed to better approximate the behavior of a real process. A parameter estimation method based on the expectation-maximization algorithm was derived for the proposed model. Experiments on speech recognition of noisy Mandarin digits were conducted to examine the effectiveness of the proposed method. Experimental results show that the proposed method can reduce the recognition error rate compared to a baseline hidden Markov model. PMID:27586781

  14. Contribution to automatic image recognition applied to robot technology

    International Nuclear Information System (INIS)

    This paper describes a method for the analysis and interpretation of the images of objects located in a plain scene which is the environment of a robot. The first part covers the recovery of the contour of objects present in the image, and discusses a novel contour-following technique based on the line arborescence concept in combination with a 'cost function' giving a quantitative assessment of contour quality. We present heuristics for moderate-cost, minimum-time arborescence coverage, which is equivalent to following probable contour lines in the image. A contour segmentation technique, invariant in the translational and rotational modes, is presented next. The second part describes a recognition method based on the above invariant encoding: the algorithm performs a preliminary screening based on coarse data derived from segmentation, followed by a comparison of forms with probable identity through application of a distance specified in terms of the invariant encoding. The last part covers the outcome of the above investigations, which have found an industrial application in the vision system of a range of robots. The system is set up in a 16-bit microprocessor and operates in real time. (author)

  15. Automatic Recognition Method for Optical Measuring Instruments Based on Machine Vision

    Institute of Scientific and Technical Information of China (English)

    SONG Le; LIN Yuchi; HAO Liguo

    2008-01-01

    Based on a comprehensive study of various algorithms, the automatic recognition of traditional ocular optical measuring instruments is realized. Taking a universal tools microscope (UTM) lens view image as an example, a 2-layer automatic recognition model for data reading is established after adopting a series of pre-processing algorithms. This model is an optimal combination of the correlation-based template matching method and a concurrent back propagation (BP) neural network. Multiple complementary feature extraction is used in generating the eigenvectors of the concurrent network. In order to improve fault-tolerance capacity, rotation invariant features based on Zernike moments are extracted from digit characters and a 4-dimensional group of the outline features is also obtained. Moreover, the operating time and reading accuracy can be adjusted dynamically by setting the threshold value. The experimental result indicates that the newly developed algorithm has optimal recognition precision and working speed. The average reading ratio can achieve 97.23%. The recognition method can automatically obtain the results of optical measuring instruments rapidly and stably without modifying their original structure, which meets the application requirements.

  16. Analysis of speech under stress using Linear techniques and Non-Linear techniques for emotion recognition system

    OpenAIRE

    A. A. Khulageand; B. V. Pathak

    2012-01-01

    Analysis of speech for recognition of stress is important for identification of emotional state of person. This can be done using ‘Linear Techniques’, which has different parameters like pitch, vocal tract spectrum, formant frequencies, Duration, MFCC etc. which are used for extraction of features from speech. TEO-CB-Auto-Env is the method which is non-linear method of features extraction. Analysis is done using TU-Berlin (Technical University of Berlin) German database. Here e...

  17. Automatic target recognition on land using three dimensional (3D laser radar and artificial neural networks

    Directory of Open Access Journals (Sweden)

    Göztepe, K.

    2013-05-01

    Full Text Available During combat, measuring the dimensions of targets is extremely important for knowing when to fire on the enemy. The importance of identifying a known target on land emphasizes the importance of techniques devoted to automatic target recognition. Although a number of object-recognition techniques have been developed in the past, none of them have provided the desired specifics for unidentified target recognition. Studies on target recognition are largely based on images that assume that images of a known target can be readily viewed under any circumstance. But this is not true for military operations conducted on various terrains under specific circumstances. Usually it is not possible to capture images of unidentified objects because of weather, inadequate equipment, or concealment. In this study, a new approach that integrates neural networks and laser radar has been developed for automatic target recognition in order to reduce the above-mentioned problems. Unlike current studies, the proposed model uses the geometric dimensions of unidentified targets in order to detect and recognise them under severe weather conditions.

  18. A method of automatic recognition of airport in complex environment from remote sensing image

    Science.gov (United States)

    Hao, Qiwei; Ni, Guoqiang; Guo, Pan; Chen, Xiaomei; Tang, Yi

    2009-11-01

    In this paper, a new method is proposed for airport recognition in complex environments. The algorithm takes all advantages of essential characteristics of the airport target. Structural characteristics of the airport are used to establish assumption process. Improved Hough transformation (HT) is used to check out those right straight-lines which stand for actual position and direction of runways. Morphological processing is used to remove road segments and isolated points. Finally, we combine these segments carefully to describe the whole airport area, and then our automatic recognition of airport target is realized.

  19. Morphological self-organizing feature map neural network with applications to automatic target recognition

    Institute of Scientific and Technical Information of China (English)

    Shijun Zhang; Zhongliang Jing; Jianxun Li

    2005-01-01

    @@ The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and realworld infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

  20. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  1. Kernel sparse coding method for automatic target recognition in infrared imagery using covariance descriptor

    Science.gov (United States)

    Yang, Chunwei; Yao, Junping; Sun, Dawei; Wang, Shicheng; Liu, Huaping

    2016-05-01

    Automatic target recognition in infrared imagery is a challenging problem. In this paper, a kernel sparse coding method for infrared target recognition using covariance descriptor is proposed. First, covariance descriptor combining gray intensity and gradient information of the infrared target is extracted as a feature representation. Then, due to the reason that covariance descriptor lies in non-Euclidean manifold, kernel sparse coding theory is used to solve this problem. We verify the efficacy of the proposed algorithm in terms of the confusion matrices on the real images consisting of seven categories of infrared vehicle targets.

  2. Automatic 3D object recognition and reconstruction based on neuro-fuzzy modelling

    Science.gov (United States)

    Samadzadegan, Farhad; Azizi, Ali; Hahn, Michael; Lucas, Curo

    Three-dimensional object recognition and reconstruction (ORR) is a research area of major interest in computer vision and photogrammetry. Virtual cities, for example, is one of the exciting application fields of ORR which became very popular during the last decade. Natural and man-made objects of cities such as trees and buildings are complex structures and automatic recognition and reconstruction of these objects from digital aerial images but also other data sources is a big challenge. In this paper a novel approach for object recognition is presented based on neuro-fuzzy modelling. Structural, textural and spectral information is extracted and integrated in a fuzzy reasoning process. The learning capability of neural networks is introduced to the fuzzy recognition process by taking adaptable parameter sets into account which leads to the neuro-fuzzy approach. Object reconstruction follows recognition seamlessly by using the recognition output and the descriptors which have been extracted for recognition. A first successful application of this new ORR approach is demonstrated for the three object classes 'buildings', 'cars' and 'trees' by using aerial colour images of an urban area of the town of Engen in Germany.

  3. Unobtrusive multimodal emotion detection in adaptive interfaces: speech and facial expressions

    NARCIS (Netherlands)

    Truong, K.P.; Leeuwen, D.A. van; Neerincx, M.A.

    2007-01-01

    Two unobtrusive modalities for automatic emotion recognition are discussed: speech and facial expressions. First, an overview is given of emotion recognition studies based on a combination of speech and facial expressions. We will identify difficulties concerning data collection, data fusion, system

  4. Face Prediction Model for an Automatic Age-invariant Face Recognition System

    OpenAIRE

    Yadav, Poonam

    2015-01-01

    Automated face recognition and identification softwares are becoming part of our daily life; it finds its abode not only with Facebook's auto photo tagging, Apple's iPhoto, Google's Picasa, Microsoft's Kinect, but also in Homeland Security Department's dedicated biometric face detection systems. Most of these automatic face identification systems fail where the effects of aging come into the picture. Little work exists in the literature on the subject of face prediction that accounts for agin...

  5. 3D facial expression recognition using SIFT descriptors of automatically detected keypoints

    OpenAIRE

    Berretti, Stefano; Ben Amor, Boulbaba; Daoudi, Mohamed; Del Bimbo, Alberto

    2011-01-01

    International audience; Methods to recognize humans' facial expressions have been proposed mainly focusing on 2D still images and videos. In this paper, the problem of person-independent facial expression recognition is addressed using the 3D geometry information extracted from the 3D shape of the face. To this end, a completely automatic approach is proposed that relies on identifying a set of facial keypoints, computing SIFT feature descriptors of depth images of the face around sample poin...

  6. Automatic facial feature extraction and expression recognition based on neural network

    OpenAIRE

    Khandait, S. P.; Dr. R.C.Thool; P.D.Khandait

    2012-01-01

    In this paper, an approach to the problem of automatic facial feature extraction from a still frontal posed image and classification and recognition of facial expression and hence emotion and mood of a person is presented. Feed forward back propagation neural network is used as a classifier for classifying the expressions of supplied face into seven basic categories like surprise, neutral, sad, disgust, fear, happy and angry. For face portion segmentation and localization, morphological image...

  7. ANALYSIS OF SPEECH UNDER STRESS USING LINEAR TECHNIQUES AND NON-LINEAR TECHNIQUES FOR EMOTION RECOGNITION SYSTEM

    Directory of Open Access Journals (Sweden)

    A. A. Khulageand

    2012-07-01

    Full Text Available Analysis of speech for recognition of stress is important for identification of emotional state of person. This can be done using ‘Linear Techniques’, which has different parameters like pitch, vocal tract spectrum, formant frequencies, Duration, MFCC etc. which are used for extraction of features from speech. TEO-CB-Auto-Env is the method which is non-linear method of features extraction. Analysis is done using TU-Berlin (Technical University of Berlin German database. Here emotion recognition is done for different emotions like neutral, happy, disgust, sad, boredom and anger. Emotion recognition is used in lie detector, database access systems, and in military for recognition of soldiers’ emotion identification during the war.

  8. Automatic speech recognizer based on the Spanish spoken in Valdivia, Chile

    Science.gov (United States)

    Sanchez, Maria L.; Poblete, Victor H.; Sommerhoff, Jorge

    2001-05-01

    The performance of an automatic speech recognizer is affected by training process (dependent on or independent of the speaker) and the size of the vocabulary. The language used in this study was the Spanish spoken in the city of Valdivia, Chile. A representative sample of 14 students and six professionals all natives of Valdivia (ten women and ten men) were used to complete the study. The sample ranged in age between 20 and 30 years old. Two systems were programmed based on the classical principles: digitalizing, end point detection, linear prediction coding, cepstral coefficients, dynamic time warping, and a final decision stage with a previous step of training: (i) one dependent speaker (15 words: five colors and ten numbers), (ii) one independent speaker (30 words: ten verbs, ten nouns, and ten adjectives). A simple didactical application, with options to choose colors, numbers and drawings of the verbs, nouns and adjectives, was designed to be used with a personal computer. In both programs, the tests carried out showed a tendency towards errors in short words with monosyllables like ``flor,'' and ``sol.'' The best results were obtained in words with three syllables like ``disparar'' and ``mojado.'' [Work supported by Proyecto DID UACh N S-200278.

  9. Contribution to automatic handwritten characters recognition. Application to optical moving characters recognition

    International Nuclear Information System (INIS)

    This paper describes a research work on computer aided vision relating to the design of a vision system which can recognize isolated handwritten characters written on a mobile support. We use a technique which consists in analyzing information contained in the contours of the polygon circumscribed to the character's shape. These contours are segmented and labelled to give a new set of features constituted by: - right and left 'profiles', - topological and algebraic unvarying properties. A new method of character's recognition induced from this representation based on a multilevel hierarchical technique is then described. In the primary level, we use a fuzzy classification with dynamic programming technique using 'profiles'. The other levels adjust the recognition by using topological and algebraic unvarying properties. Several results are presented and an accuracy of 99 pc was reached for handwritten numeral characters, thereby attesting the robustness of our algorithm. (author)

  10. Definition and automatic anatomy recognition of lymph node zones in the pelvis on CT images

    Science.gov (United States)

    Liu, Yu; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Guo, Shuxu; Attor, Rosemary; Reinicke, Danica; Torigian, Drew A.

    2016-03-01

    Currently, unlike IALSC-defined thoracic lymph node zones, no explicitly provided definitions for lymph nodes in other body regions are available. Yet, definitions are critical for standardizing the recognition, delineation, quantification, and reporting of lymphadenopathy in other body regions. Continuing from our previous work in the thorax, this paper proposes a standardized definition of the grouping of pelvic lymph nodes into 10 zones. We subsequently employ our earlier Automatic Anatomy Recognition (AAR) framework designed for body-wide organ modeling, recognition, and delineation to actually implement these zonal definitions where the zones are treated as anatomic objects. First, all 10 zones and key anatomic organs used as anchors are manually delineated under expert supervision for constructing fuzzy anatomy models of the assembly of organs together with the zones. Then, optimal hierarchical arrangement of these objects is constructed for the purpose of achieving the best zonal recognition. For actual localization of the objects, two strategies are used -- optimal thresholded search for organs and one-shot method for the zones where the known relationship of the zones to key organs is exploited. Based on 50 computed tomography (CT) image data sets for the pelvic body region and an equal division into training and test subsets, automatic zonal localization within 1-3 voxels is achieved.

  11. Hierarchical Model-Based Activity Recognition With Automatic Low-Level State Discovery

    Directory of Open Access Journals (Sweden)

    Justin Muncaster

    2007-09-01

    Full Text Available Activity recognition in video streams is increasingly important for both the computer vision and artificial intelligence communities. Activity recognition has many applications in security and video surveillance. Ultimately in such applications one wishes to recognize complex activities, which can be viewed as combination of simple activities. In this paper, we present a general framework of a Dlevel dynamic Bayesian network to perform complex activity recognition. The levels of the network are constrained to enforce state hierarchy while the Dth level models the duration of simplest event. Moreover, in this paper we propose to use the deterministic annealing clustering method to automatically define the simple activities, which corresponds to the low level states of observable levels in a Dynamic Bayesian Networks. We used real data sets for experiments. The experimental results show the effectiveness of our proposed method.

  12. A Novel Algorithm for Acoustic and Visual Classifiers Decision Fusion in Audio-Visual Speech Recognition System

    Directory of Open Access Journals (Sweden)

    P.S. Sathidevi

    2010-03-01

    Full Text Available Audio-visual speech recognition (AVSR using acoustic and visual signals of speech have received attention recently because of its robustness in noisy environments. Perceptual studies also support this approach by emphasizing the importance of visual information for speech recognition in humans. An important issue in decision fusion based AVSR system is how to obtain the appropriate integration weight for the speech modalities to integrate and ensure the combined AVSR system’s performances better than that of the audio-only and visual-only systems under various noise conditions. To solve this issue, we present a genetic algorithm (GA based optimization scheme to obtain the appropriate integration weight from the relative reliability of each modality. The performance of the proposed GA optimized reliability-ratio based weight estimation scheme is demonstrated via single speaker, mobile functions isolated word recognition experiments. The results show that the proposed scheme improves robust recognition accuracy over the conventional unimodal systems and the baseline reliability ratio-based AVSR system under various signal to noise ratio conditions.

  13. Investigating an Application of Speech-to-Text Recognition: A Study on Visual Attention and Learning Behaviour

    Science.gov (United States)

    Huang, Y-M.; Liu, C-J.; Shadiev, Rustam; Shen, M-H.; Hwang, W-Y.

    2015-01-01

    One major drawback of previous research on speech-to-text recognition (STR) is that most findings showing the effectiveness of STR for learning were based upon subjective evidence. Very few studies have used eye-tracking techniques to investigate visual attention of students on STR-generated text. Furthermore, not much attention was paid to…

  14. Computer-Mediated Input, Output and Feedback in the Development of L2 Word Recognition from Speech

    Science.gov (United States)

    Matthews, Joshua; Cheng, Junyu; O'Toole, John Mitchell

    2015-01-01

    This paper reports on the impact of computer-mediated input, output and feedback on the development of second language (L2) word recognition from speech (WRS). A quasi-experimental pre-test/treatment/post-test research design was used involving three intact tertiary level English as a Second Language (ESL) classes. Classes were either assigned to…

  15. Comparative Study on Feature Selection and Fusion Schemes for Emotion Recognition from Speech

    Directory of Open Access Journals (Sweden)

    Santiago Planet

    2012-09-01

    Full Text Available The automatic analysis of speech to detect affective states may improve the way users interact with electronic devices. However, the analysis only at the acoustic level could be not enough to determine the emotion of a user in a realistic scenario. In this paper we analyzed the spontaneous speech recordings of the FAU Aibo Corpus at the acoustic and linguistic levels to extract two sets of features. The acoustic set was reduced by a greedy procedure selecting the most relevant features to optimize the learning stage. We compared two versions of this greedy selection algorithm by performing the search of the relevant features forwards and backwards. We experimented with three classification approaches: Naïve-Bayes, a support vector machine and a logistic model tree, and two fusion schemes: decision-level fusion, merging the hard-decisions of the acoustic and linguistic classifiers by means of a decision tree; and feature-level fusion, concatenating both sets of features before the learning stage. Despite the low performance achieved by the linguistic data, a dramatic improvement was achieved after its combination with the acoustic information, improving the results achieved by this second modality on its own. The results achieved by the classifiers using the parameters merged at feature level outperformed the classification results of the decision-level fusion scheme, despite the simplicity of the scheme. Moreover, the extremely reduced set of acoustic features obtained by the greedy forward search selection algorithm improved the results provided by the full set.

  16. Speech Emotion Recognition Algorithm Based on SVM%基于SVM的语音情感识别算法

    Institute of Scientific and Technical Information of China (English)

    朱菊霞; 吴小培; 吕钊

    2011-01-01

    为有效提高语音情感识别系统的识别正确率,提出一种基于SVM的语音情感识别算法.该算法提取语音信号的能量、基音频率及共振峰等参数作为情感特征,采用SVM(Support Vector Machine,支持向量机)方法对情感信号进行建模与识别.在仿真环境下的情感识别实验中,所提算法相比较人工神经网络的ACON(All Class inone Network,"一对多")和OCON(One class in one network,"一对一")方法识别正确率分别提高了7.06%和7.21%.实验结果表明基于SVM的语音情感识别算法能够对语音情感信号进行较好地识别.%In order to improve recognition accuracy of the speech emotion recognition system effectively, a speech emotion recognition algorithm based on SVM is proposed. In the proposed algorithm, some parameters extracted from speech signals, such as: energy, pitch frequency and formant, are used as emotional features. Furthermore, an emotion recognition model is established with SVM method. Simulation environment experiential results reveal that the recognition ratio of the proposed algorithm obtains the relative increasing of 7.06% and 7.21% compared with artificial neural networks such as ACON (All Class in one Network, "one to many") and OCON (One class in one network, "one to one") methods. The result of the experiment shows that the speech emotion recognition algorithm based on SVM can improve the performance of the emotion recognition system effectively.

  17. 声纹识别中合成语音的鲁棒性%Robust Speaker Recognition against Synthetic Speech

    Institute of Scientific and Technical Information of China (English)

    陈联武; 郭武; 戴礼荣

    2011-01-01

    随着以隐马尔科夫模型为基础的语音合成技术的发展,冒认者很容易利用该技术生成具有目标说话人特性的合成语音,这对现有的声纹识别系统构成巨大威胁.针对此问题,文中从统计学的角度分析自然语音与合成语音在实倒谱上的区别,并提出对合成语音具有鲁棒性的声纹识别系统.实验结果初步表明,相比于传统的声纹识别系统,在对自然语音的等错误率不变的情况下,该系统对合成语音的错误接受率由99.2%降为0.%With the development of the hidden markov model ( HMM) based speech synthesis technology, it is easy for impostors to produce synthetic speech with the specific speakers characteristics, which becomes an enormous threat to the existing speaker recognition system. In this paper, the difference between natural speech and synthetic speech is investigated on the real part of cepstrum. And a speaker recognition system is proposed which is robust against synthetic speech. Experimental results demonstrate that the false accept rate (FAR) for synthetic speech is zero in the proposed system, while that of the existing speaker recognition system is 99. 2% with the equal error rate (EER) for natural speech unchanged.

  18. Improving the Syllable-Synchronous Network Search Algorithm for Word Decoding in Continuous Chinese Speech Recognition

    Institute of Scientific and Technical Information of China (English)

    郑方; 武健; 宋战江

    2000-01-01

    The previously proposed syllable-synchronous network search (SSNS) algorithm plays a very important role in the word decoding of the continuous Chinese speech recognition and achieves satisfying performance. Several related key factors that may affect the overall word decoding effect are carefully studied in this paper, including the perfecting of the vocabulary, the big-discount Turing re-estimating of the N-Gram probabilities, and the managing of the searching path buffers. Based on these discussions, corresponding approaches to improving the SSNS algorithm are proposed. Compared with the previous version of SSNS algorithm, the new version decreases the Chinese character error rate (CCER) in the word decoding by 42.1% across a database consisting of a large number of testing sentences (syllable strings).

  19. Hindi Digits Recognition System on Speech Data Collected in Different Natural Noise Environments

    Directory of Open Access Journals (Sweden)

    Babita Saxena

    2015-02-01

    Full Text Available This paper presents a baseline digits speech recogn izer for Hindi language. The recording environment is different for all speakers, since th e data is collected in their respective homes. The different environment refers to vehicle horn no ises in some road facing rooms, internal background noises in some rooms like opening doors, silence in some rooms etc. All these recordings are used for training acoustic model. Th e Acoustic Model is trained on 8 speakers’ audio data. The vocabulary size of the recognizer i s 10 words. HTK toolkit is used for building acoustic model and evaluating the recognition rate of the recognizer. The efficiency of the recognizer developed on recorded data, is shown at the end of the paper and possible directions for future research work are suggested.

  20. An Additive and Convolutive Bias Compensation Algorithm for Telephone Speech Recognition1)

    Institute of Scientific and Technical Information of China (English)

    HANZhao-Bing; ZHANGShu-Wu; XUBo; HUANGTai-Yi

    2004-01-01

    A Vector piecewise polynomial (VPP) approximation algorithm is proposed for environ-ment compensation of speech signals degraded by both additive and convolutive noises. By investi-gating the model of the telephone environment, we propose a piecewise polynomial, namely twolinear polynomials and a quadratic polynomial, to approximate the environment function precisely.The VPP is applied either to the stationary noise, or to the non stationary noise. In the first case,the batch EM is used in log-spectral domain; in the second case the recursive EM with iterativestochastic approximation is developed in cepstral domain. Both approaches are based on the mini-mum mean squared error (MMSE) sense. Experimental results are presented on the application ofthis approach in improving the performance of Mandarin large vocabulary continuous speech recog-nition (LVCSR) due to the background noises and different transmission channels (such as fixedtelephone line and GSM). The method can reduce the average character error rate (CER) by a-bout 18%.

  1. Influence of tinnitus percentage index of speech recognition in patients with normal hearing

    Directory of Open Access Journals (Sweden)

    Urnau, Daila

    2010-12-01

    Full Text Available Introduction: The understanding of speech is one of the most important measurable aspects of human auditory function. Tinnitus affects the quality of life, impairing communication. Objective: To investigate possible changes in the Percentage Index of Speech Recognition (SDT in individuals with tinnitus have normal hearing and examining the relationship between tinnitus, gender and age. Methods:A retrospective study by analyzing the records of 82 individuals of both genders, aged 21-70 years, totaling 128 ears with normal hearing. The ears were analyzed separately, and divided into control group, no complaints of tinnitus and group study, with complaints of tinnitus. The variables gender and age groups and examined the influence of tinnitus in the SDT. It was considered normal, the percentage of 100% correct and changed, and the value between 88-96%. These criteria were adopted, since the percentage below 88% correct is found in individuals with sensorineural hearing loss. Results:There was no statistically significant difference between the variables age and tinnitus, and tinnitus SDT, only gender and tinnitus. The prevalence of tinnitus in females (56%, higher incidence of tinnitus in the age group 31-40 years (41.67% and fewer from 41 to 50 years (18.75% and on the SDT there was a greater percentage change in individuals with tinnitus (61.11%. Conclusion: The buzz does not interfere with SDT and there is no relationship between tinnitus and age, only between tinnitus and gender.

  2. AUTOMATIC RECOGNITION OF PIPING SYSTEM FROM LARGE-SCALE TERRESTRIAL LASER SCAN DATA

    Directory of Open Access Journals (Sweden)

    K. Kawashima

    2012-09-01

    Full Text Available Recently, changes in plant equipment have been becoming more frequent because of the short lifetime of the products, and constructing 3D shape models of existing plants (as-built models from large-scale laser scanned data is expected to make their rebuilding processes more efficient. However, the laser scanned data of the existing plant has massive points, captures tangled objects and includes a large amount of noises, so that the manual reconstruction of a 3D model is very time-consuming and costs a lot. Piping systems especially, account for the greatest proportion of plant equipment. Therefore, the purpose of this research was to propose an algorithm which can automatically recognize a piping system from terrestrial laser scan data of the plant equipment. The straight portion of pipes, connecting parts and connection relationship of the piping system can be recognized in this algorithm. Eigenvalue analysis of the point clouds and of the normal vectors allows for the recognition. Using only point clouds, the recognition algorithm can be applied to registered point clouds and can be performed in a fully automatic way. The preliminary results of the recognition for large-scale scanned data from an oil rig plant have shown the effectiveness of the algorithm.

  3. Strategies for distant speech recognitionin reverberant environments

    Science.gov (United States)

    Delcroix, Marc; Yoshioka, Takuya; Ogawa, Atsunori; Kubo, Yotaro; Fujimoto, Masakiyo; Ito, Nobutaka; Kinoshita, Keisuke; Espi, Miquel; Araki, Shoko; Hori, Takaaki; Nakatani, Tomohiro

    2015-12-01

    Reverberation and noise are known to severely affect the automatic speech recognition (ASR) performance of speech recorded by distant microphones. Therefore, we must deal with reverberation if we are to realize high-performance hands-free speech recognition. In this paper, we review a recognition system that we developed at our laboratory to deal with reverberant speech. The system consists of a speech enhancement (SE) front-end that employs long-term linear prediction-based dereverberation followed by noise reduction. We combine our SE front-end with an ASR back-end that uses neural networks for acoustic and language modeling. The proposed system achieved top scores on the ASR task of the REVERB challenge. This paper describes the different technologies used in our system and presents detailed experimental results that justify our implementation choices and may provide hints for designing distant ASR systems.

  4. On the Use of Evolutionary Algorithms to Improve the Robustness of Continuous Speech Recognition Systems in Adverse Conditions

    Directory of Open Access Journals (Sweden)

    Sid-Ahmed Selouani

    2003-07-01

    Full Text Available Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT in the mel-frequency domain with a genetic algorithm (GA to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs varying from 16 dB to −4 dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.

  5. On the Use of Evolutionary Algorithms to Improve the Robustness of Continuous Speech Recognition Systems in Adverse Conditions

    Science.gov (United States)

    Selouani, Sid-Ahmed; O'Shaughnessy, Douglas

    2003-12-01

    Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR) systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT) in the mel-frequency domain with a genetic algorithm (GA) to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs) varying from 16 dB to[InlineEquation not available: see fulltext.] dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.

  6. Assessing the impact of graphical quality on automatic text recognition in digital maps

    Science.gov (United States)

    Chiang, Yao-Yi; Leyk, Stefan; Honarvar Nazari, Narges; Moghaddam, Sima; Tan, Tian Xiang

    2016-08-01

    Converting geographic features (e.g., place names) in map images into a vector format is the first step for incorporating cartographic information into a geographic information system (GIS). With the advancement in computational power and algorithm design, map processing systems have been considerably improved over the last decade. However, the fundamental map processing techniques such as color image segmentation, (map) layer separation, and object recognition are sensitive to minor variations in graphical properties of the input image (e.g., scanning resolution). As a result, most map processing results would not meet user expectations if the user does not "properly" scan the map of interest, pre-process the map image (e.g., using compression or not), and train the processing system, accordingly. These issues could slow down the further advancement of map processing techniques as such unsuccessful attempts create a discouraged user community, and less sophisticated tools would be perceived as more viable solutions. Thus, it is important to understand what kinds of maps are suitable for automatic map processing and what types of results and process-related errors can be expected. In this paper, we shed light on these questions by using a typical map processing task, text recognition, to discuss a number of map instances that vary in suitability for automatic processing. We also present an extensive experiment on a diverse set of scanned historical maps to provide measures of baseline performance of a standard text recognition tool under varying map conditions (graphical quality) and text representations (that can vary even within the same map sheet). Our experimental results help the user understand what to expect when a fully or semi-automatic map processing system is used to process a scanned map with certain (varying) graphical properties and complexities in map content.

  7. Interpreting sign components from accelerometer and sEMG data for automatic sign language recognition.

    Science.gov (United States)

    Li, Yun; Chen, Xiang; Zhang, Xu; Wang, Kongqiao; Yang, Jihai

    2011-01-01

    The identification of constituent components of each sign gesture is a practical way of establishing large-vocabulary sign language recognition (SLR) system. Aiming at developing such a system using portable accelerometer (ACC) and surface electromyographic (sEMG) sensors, this work proposes a method for automatic SLR at the component level. The preliminary experimental results demonstrate the effectiveness of the proposed method and the feasibility of interpreting sign components from ACC and sEMG data. Our study improves the performance of SLR based on ACC and sEMG sensors and will promote the realization of a large-vocabulary portable SLR system. PMID:22255059

  8. All Optical Three Dimensional Spatio-Temporal Correlator for Automatic Event Recognition Using Multiphoton Atomic System

    CERN Document Server

    Monjur, Mehjabin S; Shahriar, Selim M

    2015-01-01

    In this paper, we model and show the simulation results of a three-dimensional spatio-temporal correlator (STC) that combines the technique of holographic correlation and photon echo based temporal pattern recognition. The STC is shift invariant in space and time. It can be used to recognize rapidly an event (e.g., a short video clip) that may be present in a large video file, and determine the temporal location of the event. It can also determine multiple matches automatically if the event occurs more than once. We show how to realize the STC using Raman transitions in Rb atomic vapor.

  9. Automatic recognition of light source from color negative films using sorting classification techniques

    Science.gov (United States)

    Sanger, Demas S.; Haneishi, Hideaki; Miyake, Yoichi

    1995-08-01

    This paper proposed a simple and automatic method for recognizing the light sources from various color negative film brands by means of digital image processing. First, we stretched the image obtained from a negative based on the standardized scaling factors, then extracted the dominant color component among red, green, and blue components of the stretched image. The dominant color component became the discriminator for the recognition. The experimental results verified that any one of the three techniques could recognize the light source from negatives of any film brands and all brands greater than 93.2 and 96.6% correct recognitions, respectively. This method is significant for the automation of color quality control in color reproduction from color negative film in mass processing and printing machine.

  10. Automatic Recognition of Sunspots in HSOS Full-Disk Solar Images

    CERN Document Server

    Zhao, Cui; Deng, YuanYong; Yang, Xiao

    2016-01-01

    A procedure is introduced to recognise sunspots automatically in solar full-disk photosphere images obtained from Huairou Solar Observing Station, National Astronomical Observatories of China. The images are first pre-processed through Gaussian algorithm. Sunspots are then recognised by the morphological Bot-hat operation and Otsu threshold. Wrong selection of sunspots is eliminated by a criterion of sunspot properties. Besides, in order to calculate the sunspots areas and the solar centre, the solar limb is extracted by a procedure using morphological closing and erosion operations and setting an adaptive threshold. Results of sunspot recognition reveal that the number of the sunspots detected by our procedure has a quite good agreement with the manual method. The sunspot recognition rate is 95% and error rate is 1.2%. The sunspot areas calculated by our method have high correlation (95%) with the area data from USAF/NOAA.

  11. Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition.

    Science.gov (United States)

    Sariyanidi, Evangelos; Gunes, Hatice; Cavallaro, Andrea

    2015-06-01

    Automatic affect analysis has attracted great interest in various contexts including the recognition of action units and basic or non-basic emotions. In spite of major efforts, there are several open questions on what the important cues to interpret facial expressions are and how to encode them. In this paper, we review the progress across a range of affect recognition applications to shed light on these fundamental questions. We analyse the state-of-the-art solutions by decomposing their pipelines into fundamental components, namely face registration, representation, dimensionality reduction and recognition. We discuss the role of these components and highlight the models and new trends that are followed in their design. Moreover, we provide a comprehensive analysis of facial representations by uncovering their advantages and limitations; we elaborate on the type of information they encode and discuss how they deal with the key challenges of illumination variations, registration errors, head-pose variations, occlusions, and identity bias. This survey allows us to identify open issues and to define future directions for designing real-world affect recognition systems. PMID:26357337

  12. Language-Independent Automatic Evaluation of Intelligibility of Chronically Hoarse Persons

    OpenAIRE

    Haderlein, Tino; Middag, Catherine; Martens, Jean-Pierre; Döllinger, Michael; Nöth, Elmar

    2014-01-01

    Objective: Automatic intelligibility assessment using automatic speech recognition is usually language specific. In this study, a language-independent approach is proposed. It uses models that are trained with Flemish speech, and it is applied to assess chronically hoarse German speakers. The research questions are here: is it possible to construct suitable acoustic features that generalize to other languages and a speech disorder, and is the generated model for intelligibility also suitable ...

  13. Modern prescription theory and application: realistic expectations for speech recognition with hearing AIDS.

    Science.gov (United States)

    Johnson, Earl E

    2013-01-01

    A major decision at the time of hearing aid fitting and dispensing is the amount of amplification to provide listeners (both adult and pediatric populations) for the appropriate compensation of sensorineural hearing impairment across a range of frequencies (e.g., 160-10000 Hz) and input levels (e.g., 50-75 dB sound pressure level). This article describes modern prescription theory for hearing aids within the context of a risk versus return trade-off and efficient frontier analyses. The expected return of amplification recommendations (i.e., generic prescriptions such as National Acoustic Laboratories-Non-Linear 2, NAL-NL2, and Desired Sensation Level Multiple Input/Output, DSL m[i/o]) for the Speech Intelligibility Index (SII) and high-frequency audibility were traded against a potential risk (i.e., loudness). The modeled performance of each prescription was compared one with another and with the efficient frontier of normal hearing sensitivity (i.e., a reference point for the most return with the least risk). For the pediatric population, NAL-NL2 was more efficient for SII, while DSL m[i/o] was more efficient for high-frequency audibility. For the adult population, NAL-NL2 was more efficient for SII, while the two prescriptions were similar with regard to high-frequency audibility. In terms of absolute return (i.e., not considering the risk of loudness), however, DSL m[i/o] prescribed more outright high-frequency audibility than NAL-NL2 for either aged population, particularly, as hearing loss increased. Given the principles and demonstrated accuracy of desensitization (reduced utility of audibility with increasing hearing loss) observed at the group level, additional high-frequency audibility beyond that of NAL-NL2 is not expected to make further contributions to speech intelligibility (recognition) for the average listener. PMID:24253361

  14. Development and Evaluation of a Speech Recognition Test for Persian Speaking Adults

    Directory of Open Access Journals (Sweden)

    Mohammad Mosleh

    2001-05-01

    Full Text Available Method and Materials: This research is carried out for development and evaluation of 25 phonemically balanced word lists for Persian speaking adults in two separate stages: development and evaluation. In the first stage, in order to balance the lists phonemically, frequency -of- occurrences of each 29phonems (6 vowels and 23 Consonants of the Persian language in adults speech are determined. This section showed some significant differences between some phonemes' frequencies. Then, all Persian monosyllabic words extracted from the Mo ‘in Persian dictionary. The semantically difficult words were refused and the appropriate words choosed according to judgment of 5 adult native speakers of Persian with high school diploma. 12 openset 25 word lists are prepared. The lists were recorded on magnetic tapes in an audio studio by a professional speaker of IRIB. "nIn the second stage, in order to evaluate the test's validity and reliability, 60 normal hearing adults (30 male, 30 female, were randomly selected and evaluated as test and retest. Findings: 1- Normal hearing adults obtained 92-1 0O scores for each list at their MCL through test-retest. 2- No significant difference was observed a/ in test-retest scores in each list (‘P>O.05 b/ between the lists at test or retest scores (P>0.05, c/between sex (P>0.05. Conclusion: This research is reliable and valid, the lists are phonemically balanced and equal in difficulty and valuable for evaluation of Persian speaking adults speech recognition.

  15. Key Technologies in Speech Emotion Recognition%语音情感识别的关键技术

    Institute of Scientific and Technical Information of China (English)

    张雪英; 孙颖; 张卫; 畅江

    2015-01-01

    语音信号中的情感信息是一种很重要的信息资源,仅靠单纯的数学模型搭建和计算来进行语音情感识别就显现出不足。情感是由外部刺激引发人的生理、心理变化,从而表现出来的一种对人或事物的感知状态,因此,将认知心理学与语音信号处理相结合有益于更好地处理情感语音。首先介绍了语音情感与人类认知的关联性,总结了该领域的最新进展和研究成果,主要包括情感数据库的建立、情感特征的提取以及情感识别网络等。其次介绍了基于认知心理学构建的模糊认知图网络在情感语音识别中的应用。接着,探讨了人脑对情感语音的认知机理,并试图把事件相关电位融合到语音情感识别中,从而提高情感语音识别的准确率,为今后情感语音识别与认知心理学交叉融合发展提出了构思与展望。%Emotional information in speech signal is an important information resource .When verbal expression is combined with human emotion ,emotional speech processing is no longer a simple mathematical model or pure calculation .Fluctuations of the mood are controlled by the brain perception ;speech signal processing based on cognitive psychology can capture emotion bet‐ter .In this paper the relevance analysis between speech emotion and human cognition is intro‐duced firstly .The recent progress in speech emotion recognition is summarized ,including the re‐view of speech emotion databases ,feature extraction and emotion recognition networks .Secondly a fuzzy cognitive map network based on cognitive psychology is introduced into emotional speech recognition .In addition ,the mechanism of the human brain for cognitive emotional speech is ex‐plored .To improve the recognition accuracy ,this report also tries to integrate event‐related poten‐tials to speech emotion recognition .This idea is the conception and prospect of speech emotion recognition

  16. Language and Speech Processing

    CERN Document Server

    Mariani, Joseph

    2008-01-01

    Speech processing addresses various scientific and technological areas. It includes speech analysis and variable rate coding, in order to store or transmit speech. It also covers speech synthesis, especially from text, speech recognition, including speaker and language identification, and spoken language understanding. This book covers the following topics: how to realize speech production and perception systems, how to synthesize and understand speech using state-of-the-art methods in signal processing, pattern recognition, stochastic modelling computational linguistics and human factor studi

  17. A Survey on Statistical Based Single Channel Speech Enhancement Techniques

    Directory of Open Access Journals (Sweden)

    Sunnydayal. V

    2014-11-01

    Full Text Available Speech enhancement is a long standing problem with various applications like hearing aids, automatic recognition and coding of speech signals. Single channel speech enhancement technique is used for enhancement of the speech degraded by additive background noises. The background noise can have an adverse impact on our ability to converse without hindrance or smoothly in very noisy environments, such as busy streets, in a car or cockpit of an airplane. Such type of noises can affect quality and intelligibility of speech. This is a survey paper and its object is to provide an overview of speech enhancement algorithms so that enhance the noisy speech signal which is corrupted by additive noise. The algorithms are mainly based on statistical based approaches. Different estimators are compared. Challenges and Opportunities of speech enhancement are also discussed. This paper helps in choosing the best statistical based technique for speech enhancement

  18. An Agent-based Framework for Speech Investigation

    OpenAIRE

    Walsh, Michael; O'Hare, G.M.P.; Carson-Berndsen, Julie

    2005-01-01

    This paper presents a novel agent-based framework for investigating speech recognition which combines statistical data and explicit phonological knowledge in order to explore strategies aimed at augmenting the performance of automatic speech recognition (ASR) systems. This line of research is motivated by a desire to provide solutions to some of the more notable problems encountered, including in particular the problematic phenomena of coarticulation, underspecified input...

  19. Research of Speech Recognition System Based on Matlab%基于Matlab的语音识别系统研究

    Institute of Scientific and Technical Information of China (English)

    王彪

    2011-01-01

    A speech recognition system based on Matlab software is designed, and record, broadcast, pretreat voice signals, subsection filtering, feature extraction and speech recognition are its main functions. This system has achieved discriminate simple voice requirements is verificated by the experiment, but some places are needed to improve, such as: whether complex voice coule be discriminated in complex environment.%设计了一个基于Matlab软件的语音识别系统,其主要功能有语音信号的录制、播放、预处理、分段滤波、特征提取以及识别语音.通过实验验证了本系统能够达到识别简单语音的要求,但仍有需改进的地方,如:能否在复杂环境下识别比较复杂的语音.

  20. Automatic recognition of cardiac arrhythmias based on the geometric patterns of Poincaré plots

    International Nuclear Information System (INIS)

    The Poincaré plot emerges as an effective tool for assessing cardiovascular autonomic regulation. It displays nonlinear characteristics of heart rate variability (HRV) from electrocardiographic (ECG) recordings and gives a global view of the long range of ECG signals. In the telemedicine or computer-aided diagnosis system, it would offer significant auxiliary information for diagnosis if the patterns of the Poincaré plots can be automatically classified. Therefore, we developed an automatic classification system to distinguish five geometric patterns of the Poincaré plots from four types of cardiac arrhythmias. The statistics features are designed on measurements and an ensemble classifier of three types of neural networks is proposed. Aiming at the difficulty to set a proper threshold for classifying the multiple categories, the threshold selection strategy is analyzed. 24 h ECG monitoring recordings from 674 patients, which have four types of cardiac arrhythmias, are adopted for recognition. For comparison, Support Vector Machine (SVM) classifiers with linear and Gaussian kernels are also applied. The experiment results demonstrate the effectiveness of the extracted features and the better performance of the designed classifier. Our study can be applied to diagnose the corresponding sinus rhythm and arrhythmia substrates disease automatically in the telemedicine and computer-aided diagnosis system. (paper)

  1. Sedimentary Micro-phase Automatic Recognition Based on BP Neural Network

    Institute of Scientific and Technical Information of China (English)

    龚声蓉; 王朝晖

    2004-01-01

    In the process of geologic prospecting and development, it is important to forecast the distribution of gritstone, master the regulation of physical parameter in the reserves mass level. Especially, it is more important to recognize to rock phase and sedimentary circumstance. In the land level, the study of sedimentary phase and micro-phase is important to prospect and develop. In this paper, an automatic approach based on ANN (Artificial Neural Networks) is proposed to recognize sedimentary phase, the corresponding system is designed after the character of well general curves is considered. Different from the approach extracting feature parameters, the proposed approach can directly process the input curves. The proposed method consists of two steps: The first step is called learning. In this step, the system creates automatically sedimentary micro-phase features by learning from the standard sedimentary micro-phase patterns such as standard electric current phase curves of the well and standard resistance rate curves of the well. The second step is called recognition. In this step, based the results of the learning step, the system classifies automatically by comparing the standard pattern curves of the well to unknown pattern curves of the well. The experiment has demonstrated that the proposed approach is more effective than those approaches used previously.

  2. Automatic recognition of cardiac arrhythmias based on the geometric patterns of Poincaré plots.

    Science.gov (United States)

    Zhang, Lijuan; Guo, Tianci; Xi, Bin; Fan, Yang; Wang, Kun; Bi, Jiacheng; Wang, Ying

    2015-02-01

    The Poincaré plot emerges as an effective tool for assessing cardiovascular autonomic regulation. It displays nonlinear characteristics of heart rate variability (HRV) from electrocardiographic (ECG) recordings and gives a global view of the long range of ECG signals. In the telemedicine or computer-aided diagnosis system, it would offer significant auxiliary information for diagnosis if the patterns of the Poincaré plots can be automatically classified. Therefore, we developed an automatic classification system to distinguish five geometric patterns of the Poincaré plots from four types of cardiac arrhythmias. The statistics features are designed on measurements and an ensemble classifier of three types of neural networks is proposed. Aiming at the difficulty to set a proper threshold for classifying the multiple categories, the threshold selection strategy is analyzed. 24 h ECG monitoring recordings from 674 patients, which have four types of cardiac arrhythmias, are adopted for recognition. For comparison, Support Vector Machine (SVM) classifiers with linear and Gaussian kernels are also applied. The experiment results demonstrate the effectiveness of the extracted features and the better performance of the designed classifier. Our study can be applied to diagnose the corresponding sinus rhythm and arrhythmia substrates disease automatically in the telemedicine and computer-aided diagnosis system. PMID:25582837

  3. Automatic anatomy recognition in post-tonsillectomy MR images of obese children with OSAS

    Science.gov (United States)

    Tong, Yubing; Udupa, Jayaram K.; Odhner, Dewey; Sin, Sanghun; Arens, Raanan

    2015-03-01

    Automatic Anatomy Recognition (AAR) is a recently developed approach for the automatic whole body wide organ segmentation. We previously tested that methodology on image cases with some pathology where the organs were not distorted significantly. In this paper, we present an advancement of AAR to handle organs which may have been modified or resected by surgical intervention. We focus on MRI of the neck in pediatric Obstructive Sleep Apnea Syndrome (OSAS). The proposed method consists of an AAR step followed by support vector machine techniques to detect the presence/absence of organs. The AAR step employs a hierarchical organization of the organs for model building. For each organ, a fuzzy model over a population is built. The model of the body region is then described in terms of the fuzzy models and a host of other descriptors which include parent to offspring relationship estimated over the population. Organs are recognized following the organ hierarchy by using an optimal threshold based search. The SVM step subsequently checks for evidence of the presence of organs. Experimental results show that AAR techniques can be combined with machine learning strategies within the AAR recognition framework for good performance in recognizing missing organs, in our case missing tonsils in post-tonsillectomy images as well as in simulating tonsillectomy images. The previous recognition performance is maintained achieving an organ localization accuracy of within 1 voxel when the organ is actually not removed. To our knowledge, no methods have been reported to date for handling significantly deformed or missing organs, especially in neck MRI.

  4. Parallel System Architecture (PSA): An efficient approach for automatic recognition of volcano-seismic events

    Science.gov (United States)

    Cortés, Guillermo; García, Luz; Álvarez, Isaac; Benítez, Carmen; de la Torre, Ángel; Ibáñez, Jesús

    2014-02-01

    Automatic recognition of volcano-seismic events is becoming one of the most demanded features in the early warning area at continuous monitoring facilities. While human-driven cataloguing is time-consuming and often an unreliable task, an appropriate machine framework allows expert technicians to focus only on result analysis and decision-making. This work presents an alternative to serial architectures used in classic recognition systems introducing a parallel implementation of the whole process: configuration, feature extraction, feature selection and classification stages are independently carried out for each type of events in order to exploit the intrinsic properties of each signal class. The system uses Gaussian Mixture Models (GMMs) to classify the database recorded at Deception Volcano Island (Antarctica) obtaining a baseline recognition rate of 84% with a cepstral-based waveform parameterization in the serial architecture. The parallel approach increases the results to close to 92% using mixture-based parameterization vectors or up to 91% when the vector size is reduced by 19% via the Discriminative Feature Selection (DFS) algorithm. Besides the result improvement, the parallel architecture represents a major step in terms of flexibility and reliability thanks to the class-focused analysis, providing an efficient tool for monitoring observatories which require real-time solutions.

  5. Managing predefined templates and macros for a departmental speech recognition system using common software.

    Science.gov (United States)

    Sistrom, C L; Honeyman, J C; Mancuso, A; Quisling, R G

    2001-09-01

    The authors have developed a networked database system to create, store, and manage predefined radiology report definitions. This was prompted by complete departmental conversion to a computer speech recognition system (SRS) for clinical reporting. The software complements and extends the capabilities of the SRS, and 2 systems are integrated by means of a simple text file format and import/export functions within each program. This report describes the functional requirements, design considerations, and implementation details of the structured report management software. The database and its interface are designed to allow all radiologists and division managers to define and update template structures relevant to their practice areas. Two key conceptual extensions supported by the template management system are the addition of a template type construct and allowing individual radiologists to dynamically share common organ system or modality-specific templates. In addition, the template manager software enables specifying predefined report structures that can be triggered at the time of dictation from printed lists of barcodes. Initial experience using the program in a regional, multisite, academic radiology practice has been positive. PMID:11720335

  6. Authenticity affects the recognition of emotions in speech: behavioral and fMRI evidence.

    Science.gov (United States)

    Drolet, Matthis; Schubotz, Ricarda I; Fischer, Julia

    2012-03-01

    The aim of the present study was to determine how authenticity of emotion expression in speech modulates activity in the neuronal substrates involved in emotion recognition. Within an fMRI paradigm, participants judged either the authenticity (authentic or play acted) or emotional content (anger, fear, joy, or sadness) of recordings of spontaneous emotions and reenactments by professional actors. When contrasting between task types, active judgment of authenticity, more than active judgment of emotion, indicated potential involvement of the theory of mind (ToM) network (medial prefrontal cortex, temporoparietal cortex, retrosplenium) as well as areas involved in working memory and decision making (BA 47). Subsequently, trials with authentic recordings were contrasted with those of reenactments to determine the modulatory effects of authenticity. Authentic recordings were found to enhance activity in part of the ToM network (medial prefrontal cortex). This effect of authenticity suggests that individuals integrate recollections of their own experiences more for judgments involving authentic stimuli than for those involving play-acted stimuli. The behavioral and functional results show that authenticity of emotional prosody is an important property influencing human responses to such stimuli, with implications for studies using play-acted emotions.

  7. Development of a two wheeled self balancing robot with speech recognition and navigation algorithm

    Science.gov (United States)

    Rahman, Md. Muhaimin; Ashik-E-Rasul, Haq, Nowab. Md. Aminul; Hassan, Mehedi; Hasib, Irfan Mohammad Al; Hassan, K. M. Rafidh

    2016-07-01

    This paper is aimed to discuss modeling, construction and development of navigation algorithm of a two wheeled self balancing mobile robot in an enclosure. In this paper, we have discussed the design of two of the main controller algorithms, namely PID algorithms, on the robot model. Simulation is performed in the SIMULINK environment. The controller is developed primarily for self-balancing of the robot and also it's positioning. As for the navigation in an enclosure, template matching algorithm is proposed for precise measurement of the robot position. The navigation system needs to be calibrated before navigation process starts. Almost all of the earlier template matching algorithms that can be found in the open literature can only trace the robot. But the proposed algorithm here can also locate the position of other objects in an enclosure, like furniture, tables etc. This will enable the robot to know the exact location of every stationary object in the enclosure. Moreover, some additional features, such as Speech Recognition and Object Detection, are added. For Object Detection, the single board Computer Raspberry Pi is used. The system is programmed to analyze images captured via the camera, which are then processed through background subtraction, followed by active noise reduction.

  8. Annotating Speech Corpus for Prosody Modeling in Indian Language Text to Speech Systems

    Directory of Open Access Journals (Sweden)

    Kiruthiga S

    2012-01-01

    Full Text Available A spoken language system, it may either be a speech synthesis or a speech recognition system, starts with building a speech corpora. We give a detailed survey of issues and a methodology that selects the appropriate speech unit in building a speech corpus for Indian language Text to Speech systems. The paper ultimately aims to improve the intelligibility of the synthesized speech in Text to Speech synthesis systems. To begin with, an appropriate text file should be selected for building the speech corpus. Then a corresponding speech file is generated and stored. This speech file is the phonetic representation of the selected text file. The speech file is processed in different levels viz., paragraphs, sentences, phrases, words, syllables and phones. These are called the speech units of the file. Researches have been done taking these units as the basic unit for processing. This paper analyses the researches done using phones, diphones, triphones, syllables and polysyllables as their basic unit for speech synthesis. The paper also provides a recommended set of combinations for polysyllables. Concatenative speech synthesis involves the concatenation of these basic units to synthesize an intelligent, natural sounding speech. The speech units are annotated with relevant prosodic information about each unit, manually or automatically, based on an algorithm. The database consisting of the units along with their annotated information is called as the annotated speech corpus. A Clustering technique is used in the annotated speech corpus that provides way to select the appropriate unit for concatenation, based on the lowest total join cost of the speech unit.

  9. Automatic Isolated-Word Arabic Sign Language Recognition System Based on Time Delay Neural Networks

    Directory of Open Access Journals (Sweden)

    Feras Fares Al Mashagba

    2014-03-01

    Full Text Available There have been a little number of attempts to develop an Arabic sign recognition system that can be used as a communication means between hearing-impaired and other people. This study introduces the first automatic isolated-word Arabic Sign Language (ArSL recognition system based on Time Delay Neural Networks (TDNN. The proposed vision-based recognition system that the user wears two simple but different colors gloves when performing the signs in the data sets within this study. The two colored regions are recognized and highlighted within each frame in the video to help in recognizing the signs. This research uses the multivariate Gaussian Mixture Model (GMM based on the characteristics of the well known Hue Saturation Lightness Model (HIS in determining the colors within the video frames. In this research the mean and covariance of the three colored region within the frames are determined and used to help us in segmenting each frame (picture into two colored regions and outlier region. Finally we propose, create and use the following four features as an input to the TDNN; the centroid position for each hand using the center of the upper area for each frame as references, the change in horizontal velocity of both hands across the frames, the change in vertical velocity of both hands across the frames and the area change for each hand across the frames. A large set of samples has been used to recognize 40 isolated words coded by 10 different signers from the Standard Arabic sign language signs. Our proposed system obtains a word recognition rate of 70.0% in testing set.

  10. Development of Portable Automatic Number Plate Recognition System on Android Mobile Phone

    Science.gov (United States)

    Mutholib, Abdul; Gunawan, Teddy S.; Chebil, Jalel; Kartiwi, Mira

    2013-12-01

    The Automatic Number Plate Recognition (ANPR) System has performed as the main role in various access control and security, such as: tracking of stolen vehicles, traffic violations (speed trap) and parking management system. In this paper, the portable ANPR implemented on android mobile phone is presented. The main challenges in mobile application are including higher coding efficiency, reduced computational complexity, and improved flexibility. Significance efforts are being explored to find suitable and adaptive algorithm for implementation of ANPR on mobile phone. ANPR system for mobile phone need to be optimize due to its limited CPU and memory resources, its ability for geo-tagging image captured using GPS coordinates and its ability to access online database to store the vehicle's information. In this paper, the design of portable ANPR on android mobile phone will be described as follows. First, the graphical user interface (GUI) for capturing image using built-in camera was developed to acquire vehicle plate number in Malaysia. Second, the preprocessing of raw image was done using contrast enhancement. Next, character segmentation using fixed pitch and an optical character recognition (OCR) using neural network were utilized to extract texts and numbers. Both character segmentation and OCR were using Tesseract library from Google Inc. The proposed portable ANPR algorithm was implemented and simulated using Android SDK on a computer. Based on the experimental results, the proposed system can effectively recognize the license plate number at 90.86%. The required processing time to recognize a license plate is only 2 seconds on average. The result is consider good in comparison with the results obtained from previous system that was processed in a desktop PC with the range of result from 91.59% to 98% recognition rate and 0.284 second to 1.5 seconds recognition time.

  11. Speech recognition in reverberant and noisy environments employing multiple feature extractors and i-vector speaker adaptation

    Science.gov (United States)

    Alam, Md Jahangir; Gupta, Vishwa; Kenny, Patrick; Dumouchel, Pierre

    2015-12-01

    The REVERB challenge provides a common framework for the evaluation of feature extraction techniques in the presence of both reverberation and additive background noise. State-of-the-art speech recognition systems perform well in controlled environments, but their performance degrades in realistic acoustical conditions, especially in real as well as simulated reverberant environments. In this contribution, we utilize multiple feature extractors including the conventional mel-filterbank, multi-taper spectrum estimation-based mel-filterbank, robust mel and compressive gammachirp filterbank, iterative deconvolution-based dereverberated mel-filterbank, and maximum likelihood inverse filtering-based dereverberated mel-frequency cepstral coefficient features for speech recognition with multi-condition training data. In order to improve speech recognition performance, we combine their results using ROVER (Recognizer Output Voting Error Reduction). For two- and eight-channel tasks, to get benefited from the multi-channel data, we also use ROVER, instead of the multi-microphone signal processing method, to reduce word error rate by selecting the best scoring word at each channel. As in a previous work, we also apply i-vector-based speaker adaptation which was found effective. In speech recognition task, speaker adaptation tries to reduce mismatch between the training and test speakers. Speech recognition experiments are conducted on the REVERB challenge 2014 corpora using the Kaldi recognizer. In our experiments, we use both utterance-based batch processing and full batch processing. In the single-channel task, full batch processing reduced word error rate (WER) from 10.0 to 9.3 % on SimData as compared to utterance-based batch processing. Using full batch processing, we obtained an average WER of 9.0 and 23.4 % on the SimData and RealData, respectively, for the two-channel task, whereas for the eight-channel task on the SimData and RealData, the average WERs found were 8

  12. Automatic facial feature extraction and expression recognition based on neural network

    CERN Document Server

    Khandait, S P; Khandait, P D

    2012-01-01

    In this paper, an approach to the problem of automatic facial feature extraction from a still frontal posed image and classification and recognition of facial expression and hence emotion and mood of a person is presented. Feed forward back propagation neural network is used as a classifier for classifying the expressions of supplied face into seven basic categories like surprise, neutral, sad, disgust, fear, happy and angry. For face portion segmentation and localization, morphological image processing operations are used. Permanent facial features like eyebrows, eyes, mouth and nose are extracted using SUSAN edge detection operator, facial geometry, edge projection analysis. Experiments are carried out on JAFFE facial expression database and gives better performance in terms of 100% accuracy for training set and 95.26% accuracy for test set.

  13. Index of Garbledness for Automatic Recognition of Plain English Texts (Short Communication

    Directory of Open Access Journals (Sweden)

    P.K. Saxena

    2010-07-01

    Full Text Available In this paper, an Index of Garbledness (IG has been defined for automatic recognition of plain English texts based on linguistic characteristics of English language without using a dictionary. It also works for continuous text without word break-up (text without blank spaces between words. These characteristics, being vague in nature, are suitably represented through fuzzy sets. A fuzzy similarity relation and a fuzzy dissimilarity measure have been used to define this Index. Based on a threshold value of the Index, one can test whether the given text (continuous without word break-up is a plain English text or not. In case the text under consideration is not a plain text, it also gives an indication to what extent it is garbled.Defence Science Journal, 2010, 60(4, pp.415-419, DOI:http://dx.doi.org/10.14429/dsj.60.501

  14. Automatically building large-scale named entity recognition corpora from Chinese Wikipedia

    Institute of Scientific and Technical Information of China (English)

    Jie ZHOU; Bi-cheng LI; Gang CHEN

    2015-01-01

    Named entity recognition (NER) is a core component in many natural language processing applications. Most NER systems rely on supervised machine learning methods, which depend on time-consuming and expensive annotations in different languages and domains. This paper presents a method for automatically building silver-standard NER corpora from Chinese Wikipedia. We refine novel and language-dependent features by exploiting the text and structure of Chinese Wikipedia. To reduce tagging errors caused by entity classification, we design four types of heuristic rules based on the characteristics of Chinese Wikipedia and train a supervised NE classifier, and a combined method is used to improve the precision and coverage. Then, we realize type identification of implicit mention by using boundary information of outgoing links. By selecting the sentences related with the domains of test data, we can train better NER models. In the experiments, large-scale NER corpora containing 2.3 million sentences are built from Chinese Wikipedia. The results show the effectiveness of automatically annotated corpora, and the trained NER models achieve the best performance when combining our silver-standard corpora with gold-standard corpora.

  15. Speech Recognition Performance in Children with Cochlear Implants Using Bimodal Stimulation

    OpenAIRE

    Rathna Kumar, S. B.; Mohanty, P.; Prakash, S. G. R.

    2010-01-01

    Cochlear implantees have considerably good speech understanding abilities in quiet surroundings. But, ambient noise poses significant difficulties in understanding speech for these individuals. Bimodal stimulation is still not used by many Indian implantees in spite of reports that bimodal stimulation is beneficial for speech understanding in noise as compared to cochlear implant alone and also prevents auditory deprivation in the un-implanted ear. The aim of the study is to evaluate the bene...

  16. Speech recognition system based on LPCC parameter%基于LPCC参数的语音识别系统

    Institute of Scientific and Technical Information of China (English)

    王彪

    2012-01-01

    为了识别简单语音,设计了一个基于LPCC参数的语音识别系统。该系统其主要功能有语音信号的录制、播放、预处理、分段滤波、特征提取以及识别语音。最后通过仿真实验验证了本系统能够达到识别简单语音的要求,但仍有需改进的地方,如:能否在复杂环境下识别比较复杂的语音。%In order to recognize simple speech,a speech recognition system based on LPCC parameter is designed,and record,broadcast,pretreat voice signals,subsection filtering,feature extraction and speech recognition are its main functions.This system has achieved discriminate simple voice requirements is verificated by the simulation experiment,but some places are needed to improve,such as:whether complex voice coule be discriminated in complex environment.

  17. An Efficient Multimodal 2D + 3D Feature-based Approach to Automatic Facial Expression Recognition

    KAUST Repository

    Li, Huibin

    2015-07-29

    We present a fully automatic multimodal 2D + 3D feature-based facial expression recognition approach and demonstrate its performance on the BU-3DFE database. Our approach combines multi-order gradient-based local texture and shape descriptors in order to achieve efficiency and robustness. First, a large set of fiducial facial landmarks of 2D face images along with their 3D face scans are localized using a novel algorithm namely incremental Parallel Cascade of Linear Regression (iPar-CLR). Then, a novel Histogram of Second Order Gradients (HSOG) based local image descriptor in conjunction with the widely used first-order gradient based SIFT descriptor are used to describe the local texture around each 2D landmark. Similarly, the local geometry around each 3D landmark is described by two novel local shape descriptors constructed using the first-order and the second-order surface differential geometry quantities, i.e., Histogram of mesh Gradients (meshHOG) and Histogram of mesh Shape index (curvature quantization, meshHOS). Finally, the Support Vector Machine (SVM) based recognition results of all 2D and 3D descriptors are fused at both feature-level and score-level to further improve the accuracy. Comprehensive experimental results demonstrate that there exist impressive complementary characteristics between the 2D and 3D descriptors. We use the BU-3DFE benchmark to compare our approach to the state-of-the-art ones. Our multimodal feature-based approach outperforms the others by achieving an average recognition accuracy of 86.32%. Moreover, a good generalization ability is shown on the Bosphorus database.

  18. 语音情感的维度特征提取与识别%Dimensional Feature Extraction and Recognition of Speech Emotion

    Institute of Scientific and Technical Information of China (English)

    李嘉; 黄程韦; 余华

    2012-01-01

    研究了情绪的维度空间模型与语音声学特征之间的关系以及语音情感的自动识别方法.介绍了基本情绪的维度空间模型,提取了唤醒度和效价度对应的情感特征,采用全局统计特征减小文本差异对情感特征的影响.研究了生气、高兴、悲伤和平静等情感状态的识别,使用高斯混合模型进行4种基本情感的建模,通过实验设定了高斯混合模型的最佳混合度,从而较好地拟合了4种情感在特征空间中的概率分布.实验结果显示,选取的语音特征适合于基本情感类别的识别,高斯混合模型对情感的建模起到了较好的效果,并且验证了二维情绪空间中,效价维度上的情意特征对语音情感识别的重要作用.%The relation between the emotion dimension space and speech features is studied. The automatic speech emotion recognition problem is addressed. A dimensional space model of basic emotions is introduced. Speech emotion features are extracted according to the arousal dimension and the valence dimension. And statistic features are used to reduce the influence of the text variations on emotional features. Anger, happiness, sadness and neutral state are studied. Gaussian mixture model is adopted for modeling and recognizing the four categories of emotions. Gaussian mixture number is optimized through experiment for the probability distribution of the 4 categories in the feature space. The experimental results show that the chosen features are suitable for recognizing basic emotions. The Gaussian mixture model achieves satisfactory classification results. The valence features in the two-dimensional space plays a more important role in emotion recognition.

  19. Testing Speech Recognition in Spanish-English Bilingual Children with the Computer-Assisted Speech Perception Assessment (CASPA): Initial Report.

    Science.gov (United States)

    García, Paula B; Rosado Rogers, Lydia; Nishi, Kanae

    2016-01-01

    This study evaluated the English version of Computer-Assisted Speech Perception Assessment (E-CASPA) with Spanish-English bilingual children. E-CASPA has been evaluated with monolingual English speakers ages 5 years and older, but it is unknown whether a separate norm is necessary for bilingual children. Eleven Spanish-English bilingual and 12 English monolingual children (6 to 12 years old) with normal hearing participated. Responses were scored by word, phoneme, consonant, and vowel. Regardless of scores, performance across three signal-to-noise ratio conditions was similar between groups, suggesting that the same norm can be used for both bilingual and monolingual children.

  20. Acceptance of speech recognition by physicians: A survey of expectations, experiences, and social influence

    DEFF Research Database (Denmark)

    Alapetite, Alexandre; Andersen, Henning Boje; Hertzum, Morten

    2009-01-01

    The present study has surveyed physician views and attitudes before and after the introduction of speech technology as a front end to an electronic medical record. At the hospital where the survey was made, speech technology recently (2006–2007) replaced traditional dictation and subsequent secre...

  1. Silent Speech Interfaces

    OpenAIRE

    Denby, B; Schultz, T.; Honda, K.; Hueber, T.; Gilbert, J.M.; Brumberg, J.S.

    2010-01-01

    Abstract The possibility of speech processing in the absence of an intelligible acoustic signal has given rise to the idea of a `silent speech? interface, to be used as an aid for the speech handicapped, or as part of a communications system operating in silence-required or high-background-noise environments. The article first outlines the emergence of the silent speech interface from the fields of speech production, automatic speech processing, speech pathology research, and telec...

  2. Objective voice and speech analysis of persons with chronic hoarseness by prosodic analysis of speech samples.

    Science.gov (United States)

    Haderlein, Tino; Döllinger, Michael; Matoušek, Václav; Nöth, Elmar

    2016-10-01

    Automatic voice assessment is often performed using sustained vowels. In contrast, speech analysis of read-out texts can be applied to voice and speech assessment. Automatic speech recognition and prosodic analysis were used to find regression formulae between automatic and perceptual assessment of four voice and four speech criteria. The regression was trained with 21 men and 62 women (average age 49.2 years) and tested with another set of 24 men and 49 women (48.3 years), all suffering from chronic hoarseness. They read the text 'Der Nordwind und die Sonne' ('The North Wind and the Sun'). Five voice and speech therapists evaluated the data on 5-point Likert scales. Ten prosodic and recognition accuracy measures (features) were identified which describe all the examined criteria. Inter-rater correlation within the expert group was between r = 0.63 for the criterion 'match of breath and sense units' and r = 0.87 for the overall voice quality. Human-machine correlation was between r = 0.40 for the match of breath and sense units and r = 0.82 for intelligibility. The perceptual ratings of different criteria were highly correlated with each other. Likewise, the feature sets modeling the criteria were very similar. The automatic method is suitable for assessing chronic hoarseness in general and for subgroups of functional and organic dysphonia. In its current version, it is almost as reliable as a randomly picked rater from a group of voice and speech therapists.

  3. Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

    Science.gov (United States)

    Mimura, Masato; Sakai, Shinsuke; Kawahara, Tatsuya

    2015-12-01

    We propose an approach to reverberant speech recognition adopting deep learning in the front-end as well as b a c k-e n d o f a r e v e r b e r a n t s p e e c h r e c o g n i t i o n s y s t e m, a n d a n o v e l m e t h o d t o i m p r o v e t h e d e r e v e r b e r a t i o n p e r f o r m a n c e of the front-end network using phone-class information. At the front-end, we adopt a deep autoencoder (DAE) for enhancing the speech feature parameters, and speech recognition is performed in the back-end using DNN-HMM acoustic models trained on multi-condition data. The system was evaluated through the ASR task in the Reverb Challenge 2014. The DNN-HMM system trained on the multi-condition training set achieved a conspicuously higher word accuracy compared to the MLLR-adapted GMM-HMM system trained on the same data. Furthermore, feature enhancement with the deep autoencoder contributed to the improvement of recognition accuracy especially in the more adverse conditions. While the mapping between reverberant and clean speech in DAE-based dereverberation is conventionally conducted only with the acoustic information, we presume the mapping is also dependent on the phone information. Therefore, we propose a new scheme (pDAE), which augments a phone-class feature to the standard acoustic features as input. Two types of the phone-class feature are investigated. One is the hard recognition result of monophones, and the other is a soft representation derived from the posterior outputs of monophone DNN. The augmented feature in either type results in a significant improvement (7-8 % relative) from the standard DAE.

  4. Towards Robust Visual Speech Recognition: Automatic Systems for Lip Reading of Dutch

    NARCIS (Netherlands)

    Chitu, A.G.

    2010-01-01

    In the last two decades we witnessed a rapid increase of the computational power governed by Moore's Law. As a side effect, the affordability of cheaper and faster CPUs increased as well. Therefore, many new “smart” devices flooded the market and made informational systems widely spread. The number

  5. Speech-enabled Computer-aided Translation

    DEFF Research Database (Denmark)

    Mesa-Lao, Bartolomé

    2014-01-01

    The present study has surveyed post-editor trainees’ views and attitudes before and after the introduction of speech technology as a front end to a computer-aided translation workbench. The aim of the survey was (i) to identify attitudes and perceptions among post-editor trainees before performing...... a post-editing task using automatic speech recognition (ASR); and (ii) to assess the degree to which post-editors’ attitudes and expectations to the use of speech technology changed after actually using it. The survey was based on two questionnaires: the first one administered before the participants...

  6. MSDP软件震相自动识别技术实现%Automatic phases recognition technology in MSDP

    Institute of Scientific and Technical Information of China (English)

    苏柱金; 黄文辉

    2015-01-01

    利用 MSDP 交互分析软件实现震相自动识别技术,技术模块主要实现 STA/LTA 触发算法、AR-AIC 震相识别方法、FilterPicker 方法和自动量取振幅方法,提高 MSDP 分析处理地震事件和进行地震速报的效率,并利用大量地震事件对震相自动识别技术进行测试和验证。%In this article,an automatic recognition technique of seismic phases in MSDP is described. The technique can be divided into two sections: automatic first P-phases recognition technique and automatic amplitude measurement technique. The whole technology module mainly includes four algorithm: STA/LTA detections algorithm,AR-AIC phase recognition algorithm,FilterPicker algorithm,automatic amplitude measurement method,which can greatly improve the efficiency of processing earthquake events and quick report.

  7. Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine

    Institute of Scientific and Technical Information of China (English)

    张军; 欧建平; 占荣辉

    2015-01-01

    In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition (EMD) and support vector machine (SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions (IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm (GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28%for tank, vehicle and soldier, respectively.

  8. The accuracy of radiology speech recognition reports in a multilingual South African teaching hospital

    International Nuclear Information System (INIS)

    Speech recognition (SR) technology, the process whereby spoken words are converted to digital text, has been used in radiology reporting since 1981. It was initially anticipated that SR would dominate radiology reporting, with claims of up to 99% accuracy, reduced turnaround times and significant cost savings. However, expectations have not yet been realised. The limited data available suggest SR reports have significantly higher levels of inaccuracy than traditional dictation transcription (DT) reports, as well as incurring greater aggregate costs. There has been little work on the clinical significance of such errors, however, and little is known of the impact of reporter seniority on the generation of errors, or the influence of system familiarity on reducing error rates. Furthermore, there have been conflicting findings on the accuracy of SR amongst users with English as first- and second-language respectively. The aim of the study was to compare the accuracy of SR and DT reports in a resource-limited setting. The first 300 SR and the first 300 DT reports generated during March 2010 were retrieved from the hospital’s PACS, and reviewed by a single observer. Text errors were identified, and then classified as either clinically significant or insignificant based on their potential impact on patient management. In addition, a follow-up analysis was conducted exactly 4 years later. Of the original 300 SR reports analysed, 25.6% contained errors, with 9.6% being clinically significant. Only 9.3% of the DT reports contained errors, 2.3% having potential clinical impact. Both the overall difference in SR and DT error rates, and the difference in ‘clinically significant’ error rates (9.6% vs. 2.3%) were statistically significant. In the follow-up study, the overall SR error rate was strikingly similar at 24.3%, 6% being clinically significant. Radiologists with second-language English were more likely to generate reports containing errors, but level of seniority

  9. Based on SQLite Technology Establishment Chinese Speech Recognition Database%基于SQLite技术的汉语语音识别数据库的建立

    Institute of Scientific and Technical Information of China (English)

    刘祥楼; 李辉; 吴香艳; 高丙坤

    2011-01-01

    建立一个适合于特定说话人识别系统的汉语语音识别数据库,对推动说话人识别技术的研究和应用具有重要意义.基于支持向量机的说话人识别系统研究和开发过程中,构建了一个基于SQLite技术的汉语语音识别数据库,通过LabVIEW平台来实现对数据库控制操作.采用无序列样本和语音数据库样本分别进行比对实验.测试结果表明:一方面,无论是采用该语音识别数据库样本还是无序样本对说话人识别系统的识别率没有改变,这充分说明本系统建立的汉语语音识别数据库具有高稳定性和可靠性;另一方面,采用语音识别数据库样本其系统识别时间却明显缩短,这是改善基于支持向量机的说话人识别系统性能的有效途径.%For the establishment of a particular model of speaker recognition system Chinese speech recognition database, speech recognition technology on the promotion of research and application of great significance, SVM Based Speaker Recognition System research and development process, a SQLite-based database Mandarin speech recognition technology is built, through the LabVIEW platform to implement control operations on the database.Sequence of sample-free and speech database to compare the experimental samples, respectively.The results show that: on the one hand, both the sample database using the speech recognition or speaker recognition disordered sample recognition rate did not change the system, which fully shows that the establishment of the Chinese speech recognition system with high stability and reliability of the database; other on the one hand, the sample database using speech recognition system to recognize their time is significantly reduced, which is to improve the SVM-based speaker recognition system performance effective way.

  10. Research progress on feature parameters of speech emotion recognition%语音情感识别中特征参数的研究进展

    Institute of Scientific and Technical Information of China (English)

    李杰; 周萍

    2012-01-01

    Speech emotion recognition is one of the new research projects, the extraction of feature parameters extraction influence the final recognition-rate efficiency directly, dimension reduction can extract the most distinguishing feature parameters of different emotions. The importance of feature parameters in speech emotion recognition is point out. The system of speech emotion recognition is introduced. The common methods of feature parameters is detailed. The common methods of dimension reduction which are used in emotion recognition are compared and analyzed. The development of speech emotion recognition in the future are prospected.%语音情感识别是近年来新兴的研究课题之一,特征参数的提取直接影响到最终的识别效率,特征降维可以提取出最能区分不同情感的特征参数.提出了特征参数在语音情感识别中的重要性,介绍了语音情感识别系统的基本组成,重点对特征参数的研究现状进行了综述,阐述了目前应用于情感识别的特征降维常用方法,并对其进行了分析比较.展望了语音情感识别的可能发展趋势.

  11. Terminology of the public relations field: corpus — automatic term recognition — terminology database

    Directory of Open Access Journals (Sweden)

    Nataša Logar Berginc

    2013-12-01

    Full Text Available The article describes an analysis of automatic term recognition results performed for single- and multi-word terms with the LUIZ term extraction system. The target application of the results is a terminology database of Public Relations and the main resource the KoRP Public Relations Corpus. Our analysis is focused on two segments: (a single-word noun term candidates, which we compare with the frequency list of nouns from KoRP and evaluate termhood on the basis of the judgements of two domain experts, and (b multi-word term candidates with verb and noun as headword. In order to better assess the performance of the system and the soundness of our approach we also performed an analysis of recall. Our results show that the terminological relevance of extracted nouns is indeed higher than that of merely frequent nouns, and that verbal phrases only rarely count as proper terms. The most productive patterns of multi-word terms with noun as a headword have the following structure: [adjective + noun], [adjective + and + adjective + noun] and [adjective + adjective + noun]. The analysis of recall shows low inter-annotator agreement, but nevertheless very satisfactory recall levels.

  12. Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

    Directory of Open Access Journals (Sweden)

    Mohammad Subhi Al-batah

    2014-01-01

    Full Text Available To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL and high-grade squamous intraepithelial lesion (HSIL. The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.

  13. ToGouR: Tour Guide Robot Visualization using Shape Recognition Pattern for Automatic Navigation

    Directory of Open Access Journals (Sweden)

    Hamzah Asyrani Bin Sulaiman

    2013-08-01

    Full Text Available The invention of autonomous robots with an increasing demand for industrial used has been caught attention from researchers and inventors. In this new sophisticated era, various types of robotsand systems have been developed and bring significant contribution to the industries, economy, and infrastructure. Thus in this project, we have develop an application for PIC based Tour Guide Robot (ToGouR where the PIC16F877A has been chosen as the main microcontroller. The application has a Graphical User Interface (GUI has allows the user to interact in interactive ways between ToGouR and the images for navigation systems. The application also has an ability to perform shape recognition for path planning and automatically make alternative ways when various shapes that have been recognized are put in the way. Various alternatives have been used to make sure this project is successful carried out. This tour guide robot is suitable to be commercialized with numerous robotics companies that would likecollaborate in delivering this project as a product and can be used for military or during any unexpected catastrophe such as one that occurred in Japan.

  14. A method for automatic situation recognition in collaborative multiplayer Serious Games

    Directory of Open Access Journals (Sweden)

    Viktor Wendel

    2015-07-01

    Full Text Available One major Serious Games challenge is adaptation of game-based learning environments towards the needs of players with heterogeneous player and learner traits. For both an instructor or an algorithmic adaptation mechanism it is vital to have knowledge about the course of the game in order to be able to recognize player intentions, potential problems, or misunderstandings - both of the game(play and the learning content. The main contribution of this paper is a mechanism to recognize high-level situations in a multiplayer Serious Game. The approach presented uses criteria and situations based on the game-state, player actions and events and calculates how likely it is that players are in a certain situation. The gathered information can be used to feed an adaptation algorithm or be presented to the instructor to improve instructor decision making. In a first evaluation, the situation recognition was able to correctly recognize all of the situations in a set of game sessions. Thus, the contribution of this paper contains a novel approach to automatically capture complex multiplayer game states influenced by unpredictable player behavior, and to interpret that information to calculate probabilities of relevant game situations to be present from which player intentions can be derived.

  15. Automatic Recognition of Isolated And Interacting Manufacturing Features In Milling Process

    Directory of Open Access Journals (Sweden)

    Abdelilah El Mesbahi

    2014-10-01

    Full Text Available Manufacturing features play an important role between design information and manufacturing activities. Recently, various efforts have been concentrated in development of automatic feature recognition systems. However, only limited number of features could be recognized, intersecting features were generally not involved. This paper presents a simple system, in which manufacturing features are easily detected using a Chain of Faces and Base of Faces (CF-BF graph. A feature is modeled by a series/parallel association of opened Chain of Faces (OCF or Closed chain of Faces (CCF that rest on a Base Face (BF. The feature is considered Perfect Manufacturing Feature (PMF if all Faces that participate in constitution of OCF/CCF are blank faces, else it is an Imperfect Manufacturing Feature (IMF. In order to establish news Virtual Faces to satisfy this necessaries condition, a judicious analysis of orientation of frontier faces that rest on BF is performed. The technique was tested on several parts taken from literature and the results were satisfying.

  16. Automatic recognition of abnormal cells in cytological tests using multispectral imaging

    Science.gov (United States)

    Gertych, A.; Galliano, G.; Bose, S.; Farkas, D. L.

    2010-03-01

    Cervical cancer is the leading cause of gynecologic disease-related death worldwide, but is almost completely preventable with regular screening, for which cytological testing is a method of choice. Although such testing has radically lowered the death rate from cervical cancer, it is plagued by low sensitivity and inter-observer variability. Moreover, its effectiveness is still restricted because the recognition of shape and morphology of nuclei is compromised by overlapping and clumped cells. Multispectral imaging can aid enhanced morphological characterization of cytological specimens. Features including spectral intensity and texture, reflecting relevant morphological differences between normal and abnormal cells, can be derived from cytopathology images and utilized in a detection/classification scheme. Our automated processing of multispectral image cubes yields nuclear objects which are subjected to classification facilitated by a library of spectral signatures obtained from normal and abnormal cells, as marked by experts. Clumps are processed separately with reduced set of signatures. Implementation of this method yields high rate of successful detection and classification of nuclei into predefined malignant and premalignant types and correlates well with those obtained by an expert. Our multispectral approach may have an impact on the diagnostic workflow of cytological tests. Abnormal cells can be automatically highlighted and quantified, thus objectivity and performance of the reading can be improved in a way which is currently unavailable in clinical setting.

  17. Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition.

    Science.gov (United States)

    Al-batah, Mohammad Subhi; Isa, Nor Ashidi Mat; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

    2014-01-01

    To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy. PMID:24707316

  18. Application of an automatic pattern recognition for aleatory signals for the surveillance of nuclear reactor and rotating machinery

    International Nuclear Information System (INIS)

    An automatic pattern recognition program PSDREC, developed for the surveillance of nuclear reactor and rotating machinery is described and the relevant theory is outlined. Pattern recognition analysis of noise signals is a powerful technique for assessing 'system normality' in dynamic systems. This program, with applies 8 statistical tests to calculated power spectral density (PSD) distribution, was earlier installed in a PDP-11/45 computer at IPEN. To analyse recorded signals from three systems, namely an operational BWR power reactor (neutron signals), a water pump and a diesel engine (vibration signals) this technique was used. Results of the tests are considered satisfactory. (Author)

  19. Differences in Speech Recognition Between Children with Attention Deficits and Typically Developed Children Disappear when Exposed to 65 dB of Auditory Noise

    Directory of Open Access Journals (Sweden)

    Göran B W Söderlund

    2016-01-01

    Full Text Available The most common neuropsychiatric condition in the in children is attention deficit hyperactivity disorder (ADHD, affecting approximately 6-9 % of the population. ADHD is distinguished by inattention and hyperactive, impulsive behaviors as well as poor performance in various cognitive tasks often leading to failures at school. Sensory and perceptual dysfunctions have also been noticed. Prior research has mainly focused on limitations in executive functioning where differences are often explained by deficits in pre-frontal cortex activation. Less notice has been given to sensory perception and subcortical functioning in ADHD. Recent research has shown that children with ADHD diagnosis have a deviant auditory brain stem response compared to healthy controls. The aim of the present study was to investigate if the speech recognition threshold differs between attentive and children with ADHD symptoms in two environmental sound conditions, with and without external noise. Previous research has namely shown that children with attention deficits can benefit from white noise exposure during cognitive tasks and here we investigate if noise benefit is present during an auditory perceptual task. For this purpose we used a modified Hagerman’s speech recognition test where children with and without attention deficits performed a binaural speech recognition task to assess the speech recognition threshold in no noise and noise conditions (65 dB. Results showed that the inattentive group displayed a higher speech recognition threshold than typically developed children (TDC and that the difference in speech recognition threshold disappeared when exposed to noise at supra threshold level. From this we conclude that inattention can partly be explained by sensory perceptual limitations that can possibly be ameliorated through noise exposure.

  20. Pitch- and Formant-Based Order Adaptation of the Fractional Fourier Transform and Its Application to Speech Recognition

    Directory of Open Access Journals (Sweden)

    Yin Hui

    2009-01-01

    Full Text Available Fractional Fourier transform (FrFT has been proposed to improve the time-frequency resolution in signal analysis and processing. However, selecting the FrFT transform order for the proper analysis of multicomponent signals like speech is still debated. In this work, we investigated several order adaptation methods. Firstly, FFT- and FrFT- based spectrograms of an artificially-generated vowel are compared to demonstrate the methods. Secondly, an acoustic feature set combining MFCC and FrFT is proposed, and the transform orders for the FrFT are adaptively set according to various methods based on pitch and formants. A tonal vowel discrimination test is designed to compare the performance of these methods using the feature set. The results show that the FrFT-MFCC yields a better discriminability of tones and also of vowels, especially by using multitransform-order methods. Thirdly, speech recognition experiments were conducted on the clean intervocalic English consonants provided by the Consonant Challenge. Experimental results show that the proposed features with different order adaptation methods can obtain slightly higher recognition rates compared to the reference MFCC-based recognizer.

  1. Speech/Music Discrimination using Entropy and Dynamism Features in a HMM Classification Framework

    OpenAIRE

    Ajmera, Jitendra; McCowan, Iain A.; Bourlard, Hervé

    2003-01-01

    In this paper, we present a new approach towards high performance speech/music discrimination on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the (local) Probability Density Function (PDF) estimators trained on clean, microphone, speech (as used in a standard large vocabulary speech recognition system) are used as a channel model at the output of which the entropy and ``dynamism'' will be measured and integrated over time through a ...

  2. Speech/Music Discrimination using Entropy and Dynamism Features in a HMM Classification Framewor

    OpenAIRE

    Ajmera, Jitendra; McCowan, Iain A.; Bourlard, Hervé

    2001-01-01

    In this paper, we present a new approach towards high performance speech/music discrimination on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the (local) Probability Density Function (PDF) estimators trained on clean, microphone, speech (as used in a standard large vocabulary speech recognition system) are used as a channel model at the output of which the entropy and ``dynamism'' will be measured and integrated over time through a ...

  3. Digital speech processing using Matlab

    CERN Document Server

    Gopi, E S

    2014-01-01

    Digital Speech Processing Using Matlab deals with digital speech pattern recognition, speech production model, speech feature extraction, and speech compression. The book is written in a manner that is suitable for beginners pursuing basic research in digital speech processing. Matlab illustrations are provided for most topics to enable better understanding of concepts. This book also deals with the basic pattern recognition techniques (illustrated with speech signals using Matlab) such as PCA, LDA, ICA, SVM, HMM, GMM, BPN, and KSOM.

  4. Detection and Separation of Speech Events in Meeting Recordings Using a Microphone Array

    Directory of Open Access Journals (Sweden)

    Yamada Miichi

    2007-01-01

    Full Text Available When applying automatic speech recognition (ASR to meeting recordings including spontaneous speech, the performance of ASR is greatly reduced by the overlap of speech events. In this paper, a method of separating the overlapping speech events by using an adaptive beamforming (ABF framework is proposed. The main feature of this method is that all the information necessary for the adaptation of ABF, including microphone calibration, is obtained from meeting recordings based on the results of speech-event detection. The performance of the separation is evaluated via ASR using real meeting recordings.

  5. Automatic Identification of Modal, Breathy and Creaky Voices

    Directory of Open Access Journals (Sweden)

    Poonam Sharma

    2013-12-01

    Full Text Available This paper presents a way for the automatic identification of different voice qualities present in a speech signal which is very beneficiary for detecting any kind of speech by an efficient speech recognition system. Proposed technique is based on three important characteristics of speech signal namely Zero Crossing Rate, Short Time Energy and Fundamental Frequency. The performance of the proposed algorithm is evaluated using the data collected from three different speakers and an overall accuracy of 87.2 % is achieved.

  6. Speech recognition with dynamic range reduction: (1) deaf and normal subjects in laboratory conditions.

    Science.gov (United States)

    Drysdale, A E; Gregory, R L

    1978-08-01

    Processing to reduce the dynamic range of speech should increase intelligibility and protect the impaired ear from overloading. There are theoretical and practical objections to using AGC devices to reduce dynamic range. These are overcome by using recently available signal processing employing high frequency carrier clipping. An increase in intelligibility of speech with this HFCC has been demonstrated, for normal subjects with simulated deafness, and for most partially hearing patients. Intelligibility is not improved for some patients; possibly due to their having learned to extract features which are lost. These patients may also benefit after training.

  7. Using automatic speech processing to study French oral vowels Contributions du traitement automatique de la parole à l'étude des voyelles orales du français

    Directory of Open Access Journals (Sweden)

    Martine Adda-Decker

    2009-10-01

    Full Text Available Automatic speech processing methods and tools can contribute to shedding light on many issues relating to phonemic variability in speech. The processing of huge amounts of speech thus allows to extract main tendencies, for which detailed interpretations then require both linguistic and methodological insights. The experimental study focuses on the variability of French oral vowels in the PFC and ESTER corpora, which are widely used both by linguists and researchers in automatic speech processing. Duration and formant measures allow to illustrate global variations depending on different parameters, which include speech style, syllable position and the speakers' regional origins. The last part addresses the phonetic realization of close-mid front vowels, using automatic classification in a Bayesian framework.

  8. A Comprehensive Noise Robust Speech Parameterization Algorithm Using Wavelet Packet Decomposition-Based Denoising and Speech Feature Representation Techniques

    Science.gov (United States)

    Kotnik, Bojan; Kačič, Zdravko

    2007-12-01

    This paper concerns the problem of automatic speech recognition in noise-intense and adverse environments. The main goal of the proposed work is the definition, implementation, and evaluation of a novel noise robust speech signal parameterization algorithm. The proposed procedure is based on time-frequency speech signal representation using wavelet packet decomposition. A new modified soft thresholding algorithm based on time-frequency adaptive threshold determination was developed to efficiently reduce the level of additive noise in the input noisy speech signal. A two-stage Gaussian mixture model (GMM)-based classifier was developed to perform speech/nonspeech as well as voiced/unvoiced classification. The adaptive topology of the wavelet packet decomposition tree based on voiced/unvoiced detection was introduced to separately analyze voiced and unvoiced segments of the speech signal. The main feature vector consists of a combination of log-root compressed wavelet packet parameters, and autoregressive parameters. The final output feature vector is produced using a two-staged feature vector postprocessing procedure. In the experimental framework, the noisy speech databases Aurora 2 and Aurora 3 were applied together with corresponding standardized acoustical model training/testing procedures. The automatic speech recognition performance achieved using the proposed noise robust speech parameterization procedure was compared to the standardized mel-frequency cepstral coefficient (MFCC) feature extraction procedures ETSI ES 201 108 and ETSI ES 202 050.

  9. Speech is Golden

    DEFF Research Database (Denmark)

    Juel Henrichsen, Peter

    2014-01-01

    on the supply side. The present article reports on a new public action strategy which has taken shape in the course of 2013-14. While Denmark is a small language area, our public sector is well organised and has considerable purchasing power. Across this past year, Danish local authorities have organised around......Most of the Danish municipalities are ready to begin to adopt automatic speech recognition, but at the same time remain nervous following a long series of bad business cases in the recent past. Complaints are voiced over costly licences and low service levels, typical effects of a de facto monopoly...

  10. Hints About Some Baseful but Indispensable Elements in Speech Recognition and Reconstruction

    Directory of Open Access Journals (Sweden)

    Mihaela Costin

    2002-07-01

    Full Text Available The cochlear implant (CI is a device used to reconstruct the hearing capabilities of a person diagnosed with total cophosis. This impairment may occur after some accidents, chemotherapy etc., the person still having an intact hearing nerve. The cochlear implant has two parts: a programmable, external part, the Digital Signal Processing (DSP device which process and transform the speech signal, and another surgically implanted part, with a certain number of electrodes (depending on brand used to stimulate the hearing nerve. The speech signal is fully processed in the DSP external device resulting the ``coded'' information on speech. This is modulated with the support of the fundamental frequency F0 and the energy impulses are inductively sent to the hearing nerve. The correct detection of this frequency is very important, determining the manner of hearing and making the difference between a "computer'' voice and a natural one. The results are applicable not only in the medical domain, but also in the Romanian speech synthesis.

  11. Dead regions in the cochlea: Implications for speech recognition and applicability of articulation index theory

    DEFF Research Database (Denmark)

    Vestergaard, Martin David

    2003-01-01

    Dead regions in the cochlea have been suggested to be responsible for failure by hearing aid users to benefit front apparently increased audibility in terms of speech intelligibility. As an alternative to the more cumbersome psychoacoustic tuning curve measurement, threshold-equalizing noise (TEN...

  12. 基于Julius的机器人语音识别系统构建%Robot Speech Recognition System Based on Julius

    Institute of Scientific and Technical Information of China (English)

    付维; 刘冬; 闵华松

    2011-01-01

    As a result of the continuous development of robot technology, speech recognition of the robot is proposed as intelligent hu- man-computer interaction. After studying the basic principles of HMM speech recognition, in the robot platform of laboratory speech recognition system for isolated words is achieved with open source HTK and Julius. Using the speech recognition system, we can extract the voice command for robot control.%随着机器人技术不断发展,本文提出机器人的语音识别这一智能人机交互方式。在研究了基于HMM语音识别基本原理的情况下,在实验室的机器人平台上,利用HTK和Julius开源平台,构建了一个孤立词的语音识别系统。利用该语音识别系统可以提取语音命令用于机器人的控制。

  13. Speech emotion recognition based on MF-DFA%基于MF-DFA的语音情感识别

    Institute of Scientific and Technical Information of China (English)

    叶吉祥; 张密霞; 龚希龄

    2011-01-01

    In order to overcome the inadequacy of emotional conventional linear argument at depicting different types of character sentiments,this paper takes the multiple fractals theory into the sound emotional identify,by analyzing the multiple fractal features on the different sound emotional state, and proposes multifractal spectrum parameters and generalized hurst index as emotional conventional parameters, combined with traditional voice acoustic features and using support vector machine (SVM) for speech emotion recognition. The resuits show that the accuracy and stability of the recognition system are ira. proved effectively through using non-linear parameters, compared with using the linear features of traditional voice recognition method. It provides a new idea for voice emotion recognition.%针对语音情感线性参数在刻画不同情感类型特征上的不足,将多重分形理论引人语音情感识别中.通过分析不同语音情感状态下的多重分形特征,提取多重分形谱参数和广义hurst指数作为新的语音情感特征参数,并结合传统语音声学特征,采用支持向量机SVM对其进行语音情感识别.试验结果表明,该方法可使系统的准确率和稳定性得到有效提高.非线性参数的引人为语音情感识别提供了一个新的思路.

  14. Emotional intelligence, not music training, predicts recognition of emotional speech prosody.

    Science.gov (United States)

    Trimmer, Christopher G; Cuddy, Lola L

    2008-12-01

    Is music training associated with greater sensitivity to emotional prosody in speech? University undergraduates (n = 100) were asked to identify the emotion conveyed in both semantically neutral utterances and melodic analogues that preserved the fundamental frequency contour and intensity pattern of the utterances. Utterances were expressed in four basic emotional tones (anger, fear, joy, sadness) and in a neutral condition. Participants also completed an extended questionnaire about music education and activities, and a battery of tests to assess emotional intelligence, musical perception and memory, and fluid intelligence. Emotional intelligence, not music training or music perception abilities, successfully predicted identification of intended emotion in speech and melodic analogues. The ability to recognize cues of emotion accurately and efficiently across domains may reflect the operation of a cross-modal processor that does not rely on gains of perceptual sensitivity such as those related to music training. PMID:19102595

  15. Emotional intelligence, not music training, predicts recognition of emotional speech prosody.

    Science.gov (United States)

    Trimmer, Christopher G; Cuddy, Lola L

    2008-12-01

    Is music training associated with greater sensitivity to emotional prosody in speech? University undergraduates (n = 100) were asked to identify the emotion conveyed in both semantically neutral utterances and melodic analogues that preserved the fundamental frequency contour and intensity pattern of the utterances. Utterances were expressed in four basic emotional tones (anger, fear, joy, sadness) and in a neutral condition. Participants also completed an extended questionnaire about music education and activities, and a battery of tests to assess emotional intelligence, musical perception and memory, and fluid intelligence. Emotional intelligence, not music training or music perception abilities, successfully predicted identification of intended emotion in speech and melodic analogues. The ability to recognize cues of emotion accurately and efficiently across domains may reflect the operation of a cross-modal processor that does not rely on gains of perceptual sensitivity such as those related to music training.

  16. SOFTWARE EFFORT ESTIMATION FRAMEWORK TO IMPROVE ORGANIZATION PRODUCTIVITY USING EMOTION RECOGNITION OF SOFTWARE ENGINEERS IN SPONTANEOUS SPEECH

    Directory of Open Access Journals (Sweden)

    B.V.A.N.S.S. Prabhakar Rao

    2015-10-01

    Full Text Available Productivity is a very important part of any organisation in general and software industry in particular. Now a day’s Software Effort estimation is a challenging task. Both Effort and Productivity are inter-related to each other. This can be achieved from the employee’s of the organization. Every organisation requires emotionally stable employees in their firm for seamless and progressive working. Of course, in other industries this may be achieved without man power. But, software project development is labour intensive activity. Each line of code should be delivered from software engineer. Tools and techniques may helpful and act as aid or supplementary. Whatever be the reason software industry has been suffering with success rate. Software industry is facing lot of problems in delivering the project on time and within the estimated budget limit. If we want to estimate the required effort of the project it is significant to know the emotional state of the team member. The responsibility of ensuring emotional contentment falls on the human resource department and the department can deploy a series of systems to carry out its survey. This analysis can be done using a variety of tools, one such, is through study of emotion recognition. The data needed for this is readily available and collectable and can be an excellent source for the feedback systems. The challenge of recognition of emotion in speech is convoluted primarily due to the noisy recording condition, the variations in sentiment in sample space and exhibition of multiple emotions in a single sentence. The ambiguity in the labels of training set also increases the complexity of problem addressed. The existing models using probabilistic models have dominated the study but present a flaw in scalability due to statistical inefficiency. The problem of sentiment prediction in spontaneous speech can thus be addressed using a hybrid system comprising of a Convolution Neural Network and

  17. Speech recognition by bilateral cochlear implant users in a cocktail-party setting

    OpenAIRE

    Loizou, Philipos C.; Hu, Yi; Litovsky, Ruth; Yu, Gongqiang; PETERS, Robert; Lake, Jennifer; Roland, Peter

    2009-01-01

    Unlike prior studies with bilateral cochlear implant users which considered only one interferer, the present study considered realistic listening situations wherein multiple interferers were present and in some cases originating from both hemifields. Speech reception thresholds were measured in bilateral users unilaterally and bilaterally in four different spatial configurations, with one and three interferers consisting of modulated noise or competing talkers. The data were analyzed in terms...

  18. Speech recall and word recognition depending on prosodic and musical cues as well as voice pitch

    OpenAIRE

    Rozanovskaya, Anna; Sokolova, Taisia

    2011-01-01

    Within this study, speech perception in different conditions was examined. The aim of the research was to compare perception results based on stimuli mode (plain spoken, rhythmic spoken or rhythmic sung stimuli) and pitch (normal, lower and higher). In the study, an experiment was conducted on 44 participants who had been asked to listen to 9 recorded sentences in Russian language (unknown to them) and write them down using Latin letters. These 9 sentences were specially prepared using differ...

  19. Term clouds as surrogates for user generated speech

    NARCIS (Netherlands)

    M. Tsagkias; M. Larson; M. de Rijke

    2008-01-01

    User generated spoken audio remains a challenge for Automatic Speech Recognition (ASR) technology and content-based audio surrogates derived from ASR-transcripts must be error robust. An investigation of the use of term clouds as surrogates for podcasts demonstrates that ASR term clouds closely appr

  20. Relating hearing loss and executive functions to hearing aid users’ preference for, and speech recognition with, different combinations of binaural noise reduction and microphone directionality

    Directory of Open Access Journals (Sweden)

    Tobias eNeher

    2014-12-01

    Full Text Available Knowledge of how executive functions relate to preferred hearing aid (HA processing is sparse and seemingly inconsistent with related knowledge for speech recognition outcomes. This study thus aimed to find out if (1 performance on a measure of reading span (RS is related to preferred binaural noise reduction (NR strength, (2 similar relations exist for two different, nonverbal measures of executive function, (3 pure-tone average hearing loss (PTA, signal-to-noise ratio (SNR, and microphone directionality (DIR also influence preferred NR strength, and (4 preference and speech recognition outcomes are similar. Sixty elderly HA users took part. Six HA conditions consisting of omnidirectional or cardioid microphones followed by inactive, moderate, or strong binaural NR as well as linear amplification were tested. Outcome was assessed at fixed SNRs using headphone simulations of a frontal target talker in a busy cafeteria. Analyses showed positive effects of active NR and DIR on preference, and negative and positive effects of, respectively, strong NR and DIR on speech recognition. Also, while moderate NR was the most preferred NR setting overall, preference for strong NR increased with SNR. No relation between RS and preference was found. However, larger PTA was related to weaker preference for inactive NR and stronger preference for strong NR for both microphone modes. Equivalent (but weaker relations between worse performance on one nonverbal measure of executive function and the HA conditions without DIR were found. For speech recognition, there were relations between HA condition, PTA, and RS, but their pattern differed from that for preference. Altogether, these results indicate that, while moderate NR works well in general, a notable proportion of HA users prefer stronger NR. Furthermore, PTA and executive functions can account for some of the variability in preference for, and speech recognition with, different binaural NR and DIR settings.

  1. Speech emotion recognition in emotional feedback for Human-Robot Interaction

    Directory of Open Access Journals (Sweden)

    Javier G. R´azuri

    2015-02-01

    Full Text Available For robots to plan their actions autonomously and interact with people, recognizing human emotions is crucial. For most humans nonverbal cues such as pitch, loudness, spectrum, speech rate are efficient carriers of emotions. The features of the sound of a spoken voice probably contains crucial information on the emotional state of the speaker, within this framework, a machine might use such properties of sound to recognize emotions. This work evaluated six different kinds of classifiers to predict six basic universal emotions from non-verbal features of human speech. The classification techniques used information from six audio files extracted from the eNTERFACE05 audio-visual emotion database. The information gain from a decision tree was also used in order to choose the most significant speech features, from a set of acoustic features commonly extracted in emotion analysis. The classifiers were evaluated with the proposed features and the features selected by the decision tree. With this feature selection could be observed that each one of compared classifiers increased the global accuracy and the recall. The best performance was obtained with Support Vector Machine and bayesNet.

  2. Adverse Conditions and ASR Techniques for Robust Speech User Interface

    Directory of Open Access Journals (Sweden)

    Urmila Shrawankar

    2011-09-01

    Full Text Available The main motivation for Automatic Speech Recognition (ASR is efficient interfaces to computers, and for the interfaces to be natural and truly useful, it should provide coverage for a large group of users. The purpose of these tasks is to further improve man-machine communication. ASR systems exhibit unacceptable degradations in performance when the acoustical environments used for training and testing the system are not the same. The goal of this research is to increase the robustness of the speech recognition systems with respect to changes in the environment. A system can be labeled as environment-independent if the recognition accuracy for a new environment is the same or higher than that obtained when the system is retrained for that environment. Attaining such performance is the dream of the researchers. This paper elaborates some of the difficulties with Automatic Speech Recognition (ASR. These difficulties are classified into Speakers characteristics and environmental conditions, and tried to suggest some techniques to compensate variations in speech signal. This paper focuses on the robustness with respect to speakers variations and changes in the acoustical environment. We discussed several different external factors that change the environment and physiological differences that affect the performance of a speech recognition system followed by techniques that are helpful to design a robust ASR system

  3. Automatic analysis of multiparty meetings

    Indian Academy of Sciences (India)

    Steve Renals

    2011-10-01

    This paper is about the recognition and interpretation of multiparty meetings captured as audio, video and other signals. This is a challenging task since the meetings consist of spontaneous and conversational interactions between a number of participants: it is a multimodal, multiparty, multistream problem. We discuss the capture and annotation of the Augmented Multiparty Interaction (AMI) meeting corpus, the development of a meeting speech recognition system, and systems for the automatic segmentation, summarization and social processing of meetings, together with some example applications based on these systems.

  4. Intermodal timing relations and audio-visual speech recognition by normal-hearing adults.

    Science.gov (United States)

    McGrath, M; Summerfield, Q

    1985-02-01

    Audio-visual identification of sentences was measured as a function of audio delay in untrained observers with normal hearing; the soundtrack was replaced by rectangular pulses originally synchronized to the closing of the talker's vocal folds and then subjected to delay. When the soundtrack was delayed by 160 ms, identification scores were no better than when no acoustical information at all was provided. Delays of up to 80 ms had little effect on group-mean performance, but a separate analysis of a subgroup of better lipreaders showed a significant trend of reduced scores with increased delay in the range from 0-80 ms. A second experiment tested the interpretation that, although the main disruptive effect of the delay occurred on a syllabic time scale, better lipreaders might be attempting to use intermodal timing cues at a phonemic level. Normal-hearing observers determined whether a 120-Hz complex tone started before or after the opening of a pair of liplike Lissajou figures. Group-mean difference limens (70.7% correct DLs) were - 79 ms (sound leading) and + 138 ms (sound lagging), with no significant correlation between DLs and sentence lipreading scores. It was concluded that most observers, whether good lipreaders or not, possess insufficient sensitivity to intermodal timing cues in audio-visual speech for them to be used analogously to voice onset time in auditory speech perception. The results of both experiments imply that delays of up to about 40 ms introduced by signal-processing algorithms in aids to lipreading should not materially affect audio-visual speech understanding.

  5. Spoken-word recognition in foreign-accented speech by L2 listeners

    NARCIS (Netherlands)

    Weber, A.C.; Broersma, M.E.; Aoyagi, M.

    2011-01-01

    Two cross-modal priming studies investigated the recognition of English words spoken with a foreign accent. Auditory English primes were either typical of a Dutch accent or typical of a Japanese accent in English and were presented to both Dutch and Japanese L2 listeners. Lexical-decision times to s

  6. Modeling and simulation of speech emotional recognition%语音情感智能识别的建模与仿真

    Institute of Scientific and Technical Information of China (English)

    黄晓峰; 彭远芳

    2012-01-01

    语音情感信息具有非线性、信息冗余、高维等复杂特点,数据含有大量噪声,传统识别模型难以消除冗余和噪声信息,导致语音情感识别正确率十分低.为了提高语音情感识别正确率,利用小波分析去噪和神经网络的非线性处理能力,提出一种基于过程神经元网络的语音情感智能识别模型.采用小波分析对语音情感信号进行去噪处理,利用主成分分析消除语音情感特征中的冗余信息,采用过程神经元网络对语音情感进行分类识别.仿真结果表明,基于过程神经元网络的识别模型的识别率比K近邻提高了13%,比支持向量机提高了8.75%,该模型是一种有效的语音情感智能识别工具.%Speech emotion information has nonlinear, redundancy and high dimension characteristics, the data has lots of noise, the traditional methods cannot eliminate the redundancy information and noise, so speech emotion recognition accuracy is quite low. In order to improve the accuracy of speech emotion recognition, this paper puts forward a speech emotion recognition model based on process neural networks strong nonlinear processing ability and wavelet analysisdenoising. The noise of speech signal is eliminated by wavelet analysis, the redundancy information is eliminated by the principal components analysis, the speech emotional signal is recognized by the process neural networks. Simulation results show that the average recognition rate of the process neural networks is higher than K neighbor by 13%, and higher than the support vector machine by 8.75%, therefore the proposed model is an effective speech emotion recognition tool.

  7. 人机交互中的语音情感识别研究进展%A survey of speech emotion recognition in human computer interaction

    Institute of Scientific and Technical Information of China (English)

    张石清; 李乐民; 赵知劲

    2013-01-01

    Speech emotion recognition is a current active research topic in the fields of signal processing,pattern recognition,artificial intelligence,human computer interaction,etc.The ultimate purpose of such research is to endow computers with emotion ability and make human computer interaction be genuinely harmonic and natural.This paper reviews the recent advance of several key problems involved in speech emotion recognition,including emotional description theory,emotional speech databases,emotional acoustic analysis as well as emotion recognition methods.In addition,the existing research problems and the future direction are presented.%语音情感识别是当前信号处理、模式识别、人工智能、人机交互等领域的热点研究课题,其研究的最终目的是赋予计算机情感能力,使得人机交互做到真正的和谐和自然.本文综述了语音情感识别所涉及到的几个关键问题,包括情感表示理论、情感语音数据库、情感声学特征分析以及情感识别方法四个方面的最新进展,并指出了研究中存在的问题及下一步发展的方向.

  8. 俄语语音识别技术的研究现状和发展趋势%Research Status and Development Trend of Russian Speech Recognition Technology

    Institute of Scientific and Technical Information of China (English)

    马延周

    2015-01-01

    Abstract:Technological advance of speech recognition facilitates intelligent human-computer interactions. And applica-tions of speech recognition technology have made human communications easier and more instantaneous. Starting with a look at the past and the present of Russian speech recognition, this paper attempts to conduct a detailed analysis on fundamental princi-ples of speech recognition, speech recognition technology based on Hammond theoretical groundwork for consecutive vast-vo-cabulary speech recognition. The paper also demonstrates steps for establishing models in Russian acoustics and speeches. As to technological barriers in speech recognition, it probes into possible way out strategies. Finally, it predicts future development di-rection and application prospects for Russian speech recognition technology.%语音识别技术的发展,推动了人机交互的智能化,语音识别实用化技术使得人们之间的交流更加方便顺畅.本文从语音识别的发展历程及俄语语音识别的现状入手,对语音识别的基本原理、基于HMM模型的语音识别技术和大词汇量连续语音识别的理论基础进行了详细分析,并介绍了俄语语音声学模型和语言模型的创建办法.针对语音识别技术面临的难点问题,探讨了应对的策略,最后对俄语语音识别技术的发展方向和应用前景作了展望.

  9. Automatic topology recognition and network monitoring based on FreeBSD

    OpenAIRE

    Macek, Petr

    2013-01-01

    The goal of the Bachelor Thesis is the creation of system tool for topology recognition and following LAN monitoring. This tool will be built on OS FreeBSD and formed like bootable image suitable for running from the USB stick. Besides the lower layers protocols TCP/IP, the protocol SNMP will be used for topology recognition and network monitoring. The tool will monitor state, network transmissions and other values at active network components too. Communication with the user will be realized...

  10. Speech acustic analysis and mfcc extraction

    Directory of Open Access Journals (Sweden)

    Olti Qirici

    2013-05-01

    Full Text Available Hereafter it will be shown a comparative approach toward the speech analysis, between different methods of characteristics extraction which would lead to a better understanding and simpler implementation of a automatic speech recognition system. Different methods are shown till now and all these methods try to rich in a better description of the signal information to be simple enough for implementation and big enough to really show the signal content. Hereby will be shown side by side some of these methods with a better view on the MFCC method which seems to be also the most used till know.

  11. An approach of crater automatic recognition based on contour digital elevation model from Chang'E Missions

    Science.gov (United States)

    Zuo, W.; Li, C.; Zhang, Z.; Li, H.; Feng, J.

    2015-12-01

    In order to provide fundamental information for exploration and related scientific research on the Moon and other planets, we propose a new automatic method to recognize craters on the lunar surface based on contour data extracted from a digital elevation model (DEM). First, we mapped 16-bits DEM to 256 gray scales for data compression, then for the purposes of better visualization, the grayscale is converted into RGB image. After that, a median filter is applied twice to DEM for data optimization, which produced smooth, continuous outlines for subsequent construction of contour plane. Considering the fact that the morphology of crater on contour plane can be approximately expressed as an ellipse or circle, we extract the outer boundaries of contour plane with the same color(gray value) as targets for further identification though a 8- neighborhood counterclockwise searching method. Then, A library of training samples is constructed based on above targets calculated from some sample DEM data, from which real crater targets are labeled as positive samples manually, and non-crater objects are labeled as negative ones. Some morphological feathers are calculated for all these samples, which are major axis (L), circumference(C), area inside the boundary(S), and radius of the largest inscribed circle(R). We use R/L, R/S, C/L, C/S, R/C, S/L as the key factors for identifying craters, and apply Fisher discrimination method on the sample library to calculate the weight of each factor and determine the discrimination formula, which is then applied to DEM data for identifying lunar craters. The method has been tested and verified with DEM data from CE-1 and CE-2, showing strong recognition ability and robustness and is applicable for the recognition of craters with various diameters and significant morphological differences, making fast and accurate automatic crater recognition possible.

  12. Contribution to automatic image recognition. Application to analysis of plain scenes of overlapping parts in robot technology

    International Nuclear Information System (INIS)

    A method for object modeling and overlapped object automatic recognition is presented. Our work is composed of three essential parts: image processing, object modeling, and evaluation of the implementation of the stated concepts. In the first part, we present a method of edge encoding which is based on a re-sampling of the data encoded according to Freeman, this method generates an isotropic, homogenous and very precise representation. The second part relates to object modeling. This important step makes much easier the recognition work. The new method proposed characterizes a model with two groups of information: the description group containing the primitives, the discrimination group containing data packs, called 'transition vectors'. Based on this original method of information organization, a 'relative learning' is able to select, to ignore and to update the information concerning the objects already learned, according to the new information to be included into the data base. The recognition is a two-pass process: the first pass determines very efficiently the presence of objects by making use of each object's particularities, and this hypothesis is either confirmed or rejected by the following fine verification pass. The last part describes in detail the experimentation results. We demonstrate the robustness of the algorithms with images in both poor lighting and overlapping objects conditions. The system, named SOFIA, has been installed into an industrial vision system series and works in real time. (author)

  13. Speech emotion recognition based on Intrinsic Time-scale Decomposition%ITD在语音情感识别中的研究

    Institute of Scientific and Technical Information of China (English)

    叶吉祥; 刘亚

    2014-01-01

    为了更好地表征语音情感状态,将固有时间尺度分解(ITD)用于语音情感特征提取。从语音信号中得到前若干阶合理旋转(PR)分量,并提取PR分量的瞬时参数特征和关联维数,以此作为新的情感特征参数,结合传统特征使用支持向量机(SVM)进行语音情感识别实验。实验结果显示,引入PR特征参数后,与传统特征的方案相比,情感识别率有了明显提高。%In order to express speech emotional state better, this paper takes the Intrinsic Time-scale Decomposition (ITD)into extracting speech emotion features, decomposes the emotion speech into a sum of Proper Rotation(PR)com-ponents, extracts instantaneous characteristic parameters and correlation dimension as new emotional characteristic param-eters, combines with traditional features and uses Support Vector Machine(SVM)for speech emotional recognition. The results show that recognition accuracy is improved obviously through using PR features parameters.

  14. A smart pattern recognition system for the automatic identification of aerospace acoustic sources

    Science.gov (United States)

    Cabell, R. H.; Fuller, C. R.

    1989-01-01

    An intelligent air-noise recognition system is described that uses pattern recognition techniques to distinguish noise signatures of five different types of acoustic sources, including jet planes, propeller planes, a helicopter, train, and wind turbine. Information for classification is calculated using the power spectral density and autocorrelation taken from the output of a single microphone. Using this system, as many as 90 percent of test recordings were correctly identified, indicating that the linear discriminant functions developed can be used for aerospace source identification.

  15. Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction

    Directory of Open Access Journals (Sweden)

    J. Del Rio Vera

    2009-01-01

    Full Text Available This paper presents a new supervised classification approach for automated target recognition (ATR in SAS images. The recognition procedure starts with a novel segmentation stage based on the Hilbert transform. A number of geometrical features are then extracted and used to classify observed objects against a previously compiled database of target and non-target features. The proposed approach has been tested on a set of 1528 simulated images created by the NURC SIGMAS sonar model, achieving up to 95% classification accuracy.

  16. Logistic Kernel Function and its Application to Speech Recognition%Logistic 核函数及其在语音识别中的应用

    Institute of Scientific and Technical Information of China (English)

    刘晓峰; 张雪英; Zizhong John Wang

    2015-01-01

    核函数是支持向量机( SVM)的核心,直接决定着SVM的性能。为提高SVM在语音识别问题中的学习能力和泛化能力,文中提出了一种 Logistic 核函数,并给出了该Logistic核函数是Mercer核的理论证明。在双螺旋、语音识别问题上的实验结果表明,该Logistic核函数是有效的,其性能优于线性、多项式、径向基、指数径向基的核函数,尤其是在语音识别中,该Logistic核函数具有更好的识别性能。%Kernel function is the core of support vector machine ( SVM) and directly affects the performance of SVM.In order to improve the learning ability and generalization ability of SVM for speech recognition, a Logistic kernel function, which is proved to be a Mercer kernel function, is presented.Experimental results on bi-spiral and speech recognition problems show that the presented Logistic kernel function is effective and performs better than linear, polynomial, radial basis and exponential radial basis kernel functions, especially in the case of speech rec-ognition.

  17. Intelligent Home Speech Recognition System Based on NL6621%语音识别技术在智能家居中的应用

    Institute of Scientific and Technical Information of China (English)

    王爱芸

    2015-01-01

    The research of intelligent home speech recognition system is very important for the development of smart home. Through the analysis of the embedded speech recognition technology and smart home control technology, voice is recorded with NL6621 board as the platform and VS1003 as audio decoding chip. And Hidden Markov Model (HMM) algorithm is used to carry out voice model training and voice matching, so that we can achieve a smart home voice con-trol system. Experiments prove that the speech control system has high recognition rate and real-time performance.%研究实用的智能家居语音识别系统,对于智能家居的发展具有重要意义。通过分析嵌入式语音识别技术以及智能家居控制技术,以 NL6621板为平台,VS1003为音频解码芯片录制语音。并利用隐马尔可夫(HMM)算法进行语音模型训练和语音匹配,实现智能家居语音控制系统。实验证明此语音控制系统具有较高的识别率和实时性。

  18. SecurityAuthentication System based on Speech Recognition%基于语音识别的安全认证系统

    Institute of Scientific and Technical Information of China (English)

    毕俊浩; 叶翰嘉; 王笑臣; 孙国梓

    2012-01-01

      Based on the analysis of the smart terminal security requirements ,speech recognition and sandbox protection technology are used to verify whether the user is authorized or not. We design and implement a safety certification system based on speech recognition in the wide used Android system. From the user speech recognition, the interactive interface and interaction protocols of the sandbox protection, and other aspects we build a detailed analysis of key technologies.%  文章在对智能终端安全性需求进行分析的基础上,将语音识别与沙盒防护技术应用于智能终端。使用者是否获得授权的验证是文章分析的一个重点问题。通过选择在广泛使用的Android系统上设计实现了一个基于语音识别的安全认证系统,从使用者声音的识别、沙盒防护的交互接口和交互协议等几个方面对系统构建的关键技术进行了详细分析。

  19. On Automatic Music Genre Recognition by Sparse Representation Classification using Auditory Temporal Modulations

    DEFF Research Database (Denmark)

    Sturm, Bob L.; Noorzad, Pardis

    2012-01-01

    A recent system combining sparse representation classification (SRC) and a perceptually-based acoustic feature (ATM) \\cite{Panagakis2009,Panagakis2009b,Panagakis2010c}, outperforms by a significant margin the state of the art in music genre recognition, e.g., \\cite{Bergstra2006}. With genre so...

  20. Automatic Eye Detection Error as a Predictor of Face Recognition Performance

    NARCIS (Netherlands)

    Dutta, Abhishek; Veldhuis, Raymond; Spreeuwers, Luuk

    2014-01-01

    Various facial image quality parameters like pose, illumination, noise, resolution, etc are known to be a predictor of face recognition performance. However, there still remain many other properties of facial images that are not captured by the existing quality parameters. In this paper, we propose