Joint decoding of multiple speech patterns for robust speech recognition

Nair, Nishanth Ulhas and Sreenivas, TV (2007) Joint decoding of multiple speech patterns for robust speech recognition. In: IEEE Workshop on Automatic Speech Recognition and Understanding, DEC 09-13, 2007, Kyoto.

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Abstract

We are addressing a new problem of improving automatic speech recognition performance, given multiple utterances of patterns from the same class. We have formulated the problem of jointly decoding K multiple patterns given a single Hidden Markov Model. It is shown that such a solution is possible by aligning the K patterns using the proposed Multi Pattern Dynamic Time Warping algorithm followed by the Constrained Multi Pattern Viterbi Algorithm The new formulation is tested in the context of speaker independent isolated word recognition for both clean and noisy patterns. When 10 percent of speech is affected by a burst noise at -5 dB Signal to Noise Ratio (local), it is shown that joint decoding using only two noisy patterns reduces the noisy speech recognition error rate to about 51 percent, when compared to the single pattern decoding using the Viterbi Algorithm. In contrast a simple maximization of individual pattern likelihoods, provides only about 7 percent reduction in error rate.

Item Type:	Conference Paper
Publisher:	IEEE
Additional Information:	Copyright 2007 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:	30 Mar 2010 11:56
Last Modified:	19 Sep 2010 05:57
URI:	http://eprints.iisc.ac.in/id/eprint/26276

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