Constructing shared-state hidden Markov Models based on a Bayesian approach

Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda

研究成果: Paper

9 引用 (Scopus)

抜粋

In this paper, we propose a method for constructing sharedstate triphone HMMs (SST-HMMs) within a practical Bayesian framework. In our method, Bayesian model selection criterion is derived for SST-HMM based on the Variational Bayesian approach. The appropriate phonetic decision tree structure of SST-HMM is found by using the criterion according to a given data set. This criterion, unlike the conventional MDL criterion, is applicable even in the case of insufficient amounts of data. We conduct two experiments on speaker independent word recognition in order to prove the effectiveness of the proposed method. The first experiment demonstrates that the Bayesian approach is valid for determining the tree structure. The second experiment demonstrates that the Bayesian criterion can design SST-HMMs with higher recognition performance than the MDL criterion when dealing with small amounts of data.

元の言語English
ページ2669-2672
ページ数4
出版物ステータスPublished - 2002 1 1
外部発表Yes
イベント7th International Conference on Spoken Language Processing, ICSLP 2002 - Denver, United States
継続期間: 2002 9 162002 9 20

Other

Other7th International Conference on Spoken Language Processing, ICSLP 2002
United States
Denver
期間02/9/1602/9/20

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

フィンガープリント Constructing shared-state hidden Markov Models based on a Bayesian approach' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Watanabe, S., Minami, Y., Nakamura, A., & Ueda, N. (2002). Constructing shared-state hidden Markov Models based on a Bayesian approach. 2669-2672. 論文発表場所 7th International Conference on Spoken Language Processing, ICSLP 2002, Denver, United States.