Browsing by Author "Usha Rani K"

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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
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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.