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

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

*この研究の対応する著者

研究成果: Article査読

2 被引用数 (Scopus)

抄録

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.

本文言語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
1
DOI
出版ステータスPublished - 2005 1月
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能

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