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Scalar quantization of features in discrete hidden Markov models

Ramachandrula, Sitaram and Thippur, Sreenivas (1997) Scalar quantization of features in discrete hidden Markov models. In: 1997 International Conference on Information, Communications and Signal Processing, ICICS, 9-12 September, Singapore, Vol.1, 537-540.

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Abstract

Traditionally, discrete hidden Markov models (DHMM) use vector quantized speech feature vectors. In this paper, we propose scalar quantization of each element of the speech feature vector in the D-HMM formulation. The alteration required in the D-HMM algorithms for this modification is discussed here. Later, a comparison is made between the performance of D-HMM based speech recognizers using scalar and vector quantization of speech features respectively. A speaker independent TIMIT vowel classification experiment is chosen for this task. It is observed that the scalar quantization of features enhances the vowel classification accuracy by 8 to 9 %, compared to VQ based D-HMM. Also, the number of HMM parameters to estimate from a given amount of training data has drastically reduced in the new idea

Item Type: Conference Paper
Additional Information: Copyright 1997 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: 22 Aug 2008
Last Modified: 19 Sep 2010 04:35
URI: http://eprints.iisc.ernet.in/id/eprint/9842

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