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

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.