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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
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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.ernet.in/id/eprint/7394

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