Continuous Speech Recognition system for Tamil language using monophone-based Hidden Markov Model

  • Authors:

  • V. Radha;C. Vimala;M. Krishnaveni

  • Affiliations:

  • Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, TamilNadu, India;Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, TamilNadu, India;Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, TamilNadu, India

  • Venue:
  • Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
  • Year:
  • 2012

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Abstract

Research in Automatic Speech Recognition (ASR) has attracted a great deal of attention over the past five decades. It aims to provide an efficient way for human to communicate with computers. With the extensive development of these systems permits the user to talk almost naturally with computers. Hence, today's researchers are mainly focusing on developing a system for recognizing continuous speech to accomplish tasks such as answering emails and creating text documents etc. But developing such system is still found to be a difficult task due its own complexity. This paper presents a Continuous Speech Recognition (CSR) system for Tamil language using Hidden Markov Model (HMM) approach. The most powerful and widely used MFCC feature extraction is used as a front-end for the proposed system. The monophone based acoustic model is chosen to recognize the given set of sentences from medium vocabulary. The results are found to be satisfactory with 92% of word recognition accuracy and 81% of sentence accuracy for the proposed developed system.