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Subject: search.filters.subject.Automatic Speech Recognition

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    A REVIEW ON AUTOMATIC SPEECH RECOGNITION ARCHITECTURE AND APPROACHES
    (International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016) Karpagavalli S; Chandra E
    Speech is the most natural communication mode for human beings. The task of speech recognition is to convert speech into a sequence of words by a computer program. Speech recognition applications enable people to use speech as another input mode to interact with applications with ease and effectively. Speech recognition interfaces in native language will enable the illiterate/semi-literate people to use the technology to greater extent without the knowledge of operating with computer keyboard or stylus. For more than three decades, a great amount of research was carried out on various aspects of speech recognition and its applications. Today many products have been developed that successfully utilize automatic speech recognition for communication between human and machines. Performance of speech recognition applications deteriorates in the presence of reverberation and even low levels of ambient noise. Robustness to noise, reverberation and characteristics of the transducer is still an unsolved problem that makes the research in the area of speech recognition still very active. A detailed study on automatic speech recognition is carried out and presented in this paper that covers the architecture, speech parameterization, methodologies, characteristics, issues, databases, tools and applications.
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    ISOLATED TAMIL WORDS SPEECH RECOGNITION USING LINEAR PREDICTIVE CODING AND NEURAL NETWORKS
    (2012-12) Sabitha P V; Karpagavalli S
    Speech Recognition is the ability of a computer to recognize general, naturally flowing utterances from a wide variety of users. In recent years, with the new generation of computing technology, speech technology becomes the next major innovation in man-machine interaction. Automatic Speech Recognition (ASR) system takes a human speech utterance as an input and returns a string of words as output. Research on speech recognition has led to variety of applications like hands free and eyes free applications, voice user interfaces, simple data entry, forensic applications, voice authentication, biometrics, robotics, air traffic controllers, preparation of medical reports, learning tools for handicapped, and reading tools for blind people. Even though research in speech recognition in English language attained certain maturity, speech interfaces in Indian languages still in the startup level. Tamil is one of the widely spoken Indian languages of the world with more than 77 million speakers. Speech interfaces in Indian languages will enable the people in various semiurban and rural parts of India to use telephones and Internet services. In the proposed work, isolated Tamil words speech recognition interface is designed using neural network algorithm. To design the system, a dataset of 10 Tamil words uttered by 20 speakers each word 5 times has been prepared. Linear predictive coding of order 8 is used for feature extraction. Back-propagation training is carried with the feature vectors extracted using LPC from the speech files in the dataset. Multilayer Perceptron algorithm in neural network is employed for recognition of the words using the trained model. An interface also designed to recognize the Tamil words uttered by the user. The average recognition rate of the system is 93.6% and for few words it gives 100% accuracy. The performance of the system is measured using word recognition rate and word error rate
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    ISOLATED TAMIL DIGIT SPEECH RECOGNITION USING TEMPLATE-BASED AND HMM-BASED APPROACHES
    (Springer, 2012-07) Karpagavalli S; Deepika R; Kokila P; Usha Rani K; Chandra E
    For more than three decades, a great amount of research was carried out on various aspects of speech signal processing and its applications. Highly successful application of speech processing is Automatic Speech Recognition (ASR). Early attempts to ASR consisted of making deterministic models of whole words in a small vocabulary and recognizing a given speech utterance as the word whose model comes closest to it. The introduction of Hidden Markov Models (HMMs) in the early 1980 provided much more powerful tool for speech recognition. And the recognition can be done for continuous speech using large vocabulary, in a speaker independent manner. Two approaches like conventional template-based and Hidden Markov Model usually performs speaker independent isolated word recognition. In this work, speaker independent isolated Tamil digit speech recognizers are designed by employing template based and HMM based approaches. The results of the approaches are compared and observed that HMM based model performs well and the word error rate is greatly reduced.
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    AUTOMATIC SPEECH RECOGNITION: ARCHITECTURE, METHODOLOGIES, CHALLENGES - A REVIEW
    (International Journal of Advanced Research in Computer Science, 2011-11) Karpagavalli S; Deepika R; Kokila P; Usha Rani K; Chandra E
    For more than three decades, a great amount of research was carried out on various aspects of speech signal processing and its applications. Highly successful application of speech processing is Automatic Speech Recognition (ASR). Early attempts to ASR consisted of making deterministic models of whole words in a small vocabulary and recognizing a given speech utterance as the word whose model comes closest to it. The introduction of Hidden Morkov Models (HMMs) in the early 1980 provided much more powerful tool for speech recognition. And the recognition can be done for continuous speech using large vocabulary, in a speaker independent manner. Today many products have been developed that successfully utilize ASR for communication between human and machines. Performance of speech recognition applications deteriorates in the presence of reverberation and even low levels of ambient noise. Robustness to noise, reverberation and characteristics of the transducer is still an unsolved problem that makes the research in the area of speech recognition still very active. A detailed study on ASR carried out and presented in this paper that covers the basic model of speech recognition, applications