Phoneme Recognition in Continuous Speech Using Large Inhomogeneous Hidden Markov Models

Sitaram, RNV and Sreenivas, TV (1994) Phoneme Recognition in Continuous Speech Using Large Inhomogeneous Hidden Markov Models. In: 1994 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1994. ICASSP-94, April 19-22, Adelaide, SA, vol.1 I/41 -1/44.

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Abstract

We present a novel scheme for phoneme recognition in continuous speech using inhomogeneous hidden Markov models (IHMMs). IHMMs can capture the temporal structure of phonemes and inter-phonemic temporal relationships effectively, with their duration dependent state transition probabilities. A two stage IHMM is proposed to capture the variabilities in speech effectively for phoneme recognition. The first stage models the acoustic and durational variabilities of all distinct sub-phonemic segments and the second stage models the acoustic and durational variability of the whole phoneme. In an experimental evaluation of the new scheme for recognizing a subset of alphabets comprising of the most confusing set of phonemes, spoken randomly and continuously, a phoneme recognition accuracy of 83% is observed.

Item Type:	Conference Paper
Publisher:	IEEE
Additional Information:	Copyright 1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	25 Aug 2008
Last Modified:	19 Sep 2010 04:28
URI:	http://eprints.iisc.ac.in/id/eprint/7394

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