7th International Conference on Spoken Language Processing

September 16-20, 2002
Denver, Colorado, USA

Constructing Shared-State Hidden Markov Models Based on a Bayesian Approach

Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda

NTT Corporation, Japan

In this paper, we propose a method for constructing shared-state triphone HMMs (SST-HMMs) within a practical Bayesian framework. In our method, Bayesian model selection criterion is derived for SSTHMM 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 insuf- ficient 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.

Full Paper

Bibliographic reference.  Watanabe, Shinji / Minami, Yasuhiro / Nakamura, Atsushi / Ueda, Naonori (2002): "Constructing shared-state hidden Markov models based on a Bayesian approach", In ICSLP-2002, 2669-2672.