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

Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda

Research output: Contribution to conferencePaper

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages2669-2672
Number of pages4
Publication statusPublished - 2002 Jan 1
Event7th International Conference on Spoken Language Processing, ICSLP 2002 - Denver, United States
Duration: 2002 Sep 162002 Sep 20

Other

Other7th International Conference on Spoken Language Processing, ICSLP 2002
CountryUnited States
CityDenver
Period02/9/1602/9/20

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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  • Cite this

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