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Kolář, J. and Müller, L. : The Application of Bayesian Information Criterion in Acoustic Model Refinement . Proc. ECMS 2003, p. 44-48, Liberec , 2003.

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Automatic speech recognition (ASR) systems usually consist of an acoustic model and a language model. This paper describes a technique of an efficient deployment of the acoustic model parameters. The acoustic model typically utilizes Continuous Density Hidden Markov Models (CDHMM). The output probability of a particular CDHMM state is represented by a Gaussian mixture density with a diagonal covariance structure. Usually, the output probability density function of each CDHMM state contains the same number of mixture components although a different number of components in individual states may yield more accurate recognition results, especially for low-resource ASR systems. The central idea is to assign more components to states where it is effective and less components to states where the increasing number of components is not warranting a significantly better description of the training data. The number of mixture components for a particular CDHMM state is chosen by optimizing the Bayesian Information Criterion (BIC).

Detail of publication

Title: The Application of Bayesian Information Criterion in Acoustic Model Refinement
Author: Kolář, J. ; Müller, L.
Language: English
Date of publication: 2 Jun 2003
Year: 2003
Type of publication: Papers in proceedings of reviewed conferences
Book title: Proc. ECMS 2003
Page: 44 - 48
ISBN: 807083708X
Address: Liberec
Date: 2 Jun 2003 - 4 Jun 2003
/ 2009-05-13 11:14:37 /


Bayesian information criterion, acoustic modeling, hidden Markov models, automatic speech recognition


 author = {Kol\'{a}\v{r}, J. and M\"{u}ller, L.},
 title = {The Application of Bayesian Information Criterion in Acoustic Model Refinement},
 year = {2003},
 address = {Liberec },
 pages = {44-48},
 booktitle = {Proc. ECMS 2003},
 ISBN = {807083708X     },
 url = {},