## MAP Estimation of Continuous Density HMM: Theory and Applications (1992)

Venue: | In: Proceedings of DARPA Speech and Natural Language Workshop |

Citations: | 26 - 6 self |

### BibTeX

@INPROCEEDINGS{Gauvain92mapestimation,

author = {Jean-luc Gauvain and Chin-hui Lee},

title = {MAP Estimation of Continuous Density HMM: Theory and Applications},

booktitle = {In: Proceedings of DARPA Speech and Natural Language Workshop},

year = {1992},

pages = {185--190},

publisher = {Morgan Kaufmann}

}

### OpenURL

### Abstract

We discuss maximum a posteriori estimation of continuous density hidden Markovmodels(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. INTRODUCTION Estimation of hidden Markov model (HMM) is usually obtained by the method of maximum likelihood (ML) [1, 10, 6] assuming that the size of the training data is large enough to provide robust estimates. This paper investigates maximum a posteriori (MAP) estimate of continuous density hidden Markov models (CDHMM). The MAP ...

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