MAP estimation of continuous density HMM: theory and applications

Authors:
Jean-Luc Gauvain;Chin-Hui Lee
Affiliations:
AT&T Bell Laboratories, Murray Hill, NJ;AT&T Bell Laboratories, Murray Hill, NJ
Venue:
HLT '91 Proceedings of the workshop on Speech and Natural Language
Year:
1992

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Abstract

We discuss maximum a posteriori estimation of continuous density hidden Markov models (CDHMM). The classical MLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities. Because of its adaptive nature, Bayesian learning serves as a unified approach for the following four speech recognition applications, namely parameter smoothing, speaker adaptation, speaker group modeling and corrective training. New experimental results on all four applications are provided to show the effectiveness of the MAP estimation approach.