Phoneme recognition in continuous speech using large inhomogeneous hidden Markov models

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. >