Vector quantization for the efficient computation of continuous density likelihoods

Authors:
Enrico Bocchieri
Affiliations:
Speech Research Dept., AT&T Bell Laboratories, Murray Hill, NJ
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Year:
1993

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Abstract

In speech recognition systems based on Continuous Observation Density Hidden Markov Models, the computation of the state likelihoods is an intensive task. This paper presents an efficient method for the computation of the likelihoods defined by weighted sums (mixtures) of Gaussians. The proposed method uses vector quantization of the input feature vector to identify a subset of Gaussian neighbors. It is here shown that, under certain conditions, instead of computing the likelihoods of all the Gaussians, one needs to compute the likelihoods of only the Gaussian neighbors. Significant (up to a factor of nine) likelihood computation reductions have been obtained on various data bases, with only a small loss of recognition accuracy.