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Improved Maximum Mutual Information Estimation Training of Continuous Density HMMs (2001)
| Venue: | Proc. EUROSPEECH |
| Citations: | 7 - 3 self |
BibTeX
@INPROCEEDINGS{Zheng01improvedmaximum,
author = {Jing Zheng and John Butzberger and Horacio Franco and Andreas Stolcke},
title = {Improved Maximum Mutual Information Estimation Training of Continuous Density HMMs},
booktitle = {Proc. EUROSPEECH},
year = {2001},
pages = {679--682}
}
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Abstract
In maximum mutual information estimation (MMIE) training, the currently widely used update equations derive from the Extended Baum-Welch (EBW) algorithm, which was originally designed for the discrete hidden Markov model (HMM) and was extended to continuous Gaussian density HMMs through approximations. We derive a new set of equations for MMIE based on a quasi-Newton algorithm, without relying on EBW. We find that by adopting a generalized form of the MMIE criterion, the H-criterion, convergence speed and recognition performance can be improved. The proposed approach has been applied to a spelled-word recognition task leading to a 21.6 % relative letter error rate reduction with respect to the standard Maximum Likelihood Estimation (MLE) training method, and showing advantages over the conventional MMIE approach in terms of both training speed and recognition accuracy. 1.
Keyphrases
maximum mutual information estimation training continuous density hmms maximum mutual information estimation standard maximum likelihood estimation update equation relative letter error rate reduction recognition performance extended baum-welch continuous gaussian density hmms quasi-newton algorithm training method convergence speed recognition accuracy spelled-word recognition task new set generalized form conventional mmie approach mmie criterion discrete hidden markov model
