Selection of shared-state Hidden Markov model structure using Bayesian criterion

Shinji Watanabe*, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


A Shared-State Hidden Markov Model (SS-HMM) has been widely used as an acoustic model in speech recognition. In this paper, we propose a method for constructing SS-HMMs within a practical Bayesian framework. Our method derives the Bayesian model selection criterion for the SS-HMM based on the variational Bayesian approach. The appropriate phonetic decision tree structure of the SS-HMM is found by using the Bayesian criterion. Unlike the conventional asymptotic criteria, this criterion is applicable even in the case of an insufficient amount of training data. The experimental results on isolated word recognition demonstrate that the proposed method does not require the tuning parameter that must be tuned according to the amount of training data, and is useful for selecting the appropriate SS-HMM structure for practical use.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalIEICE Transactions on Information and Systems
Issue number1
Publication statusPublished - 2005 Jan
Externally publishedYes


  • Bayesian criterion
  • Model structure selection
  • Shared-state HMM
  • Speech recognition
  • Variational bayes

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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