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

Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda

研究成果: Article

1 引用 (Scopus)

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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.

元の言語English
ページ(範囲)1-9
ページ数9
ジャーナルIEICE Transactions on Information and Systems
E88-D
発行部数1
DOI
出版物ステータスPublished - 2005 1
外部発表Yes

ASJC Scopus subject areas

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

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