Automatic Generation of Non-uniform and Context-Dependent HMMs Based on the Variational Bayesian Approach

  • Authors:

  • Takatoshi Jitsuhiro;Satoshi Nakamura

  • Affiliations:

  • The authors are with the Spoken Language Translation Research Laboratories, Advanced Telecommunications Research Institute International, Keihanna Science City, Kyoto-fu, 619-0288 Japan. E-mail: t ...;The authors are with the Spoken Language Translation Research Laboratories, Advanced Telecommunications Research Institute International, Keihanna Science City, Kyoto-fu, 619-0288 Japan. E-mail: t ...

  • Venue:
  • IEICE - Transactions on Information and Systems
  • Year:
  • 2005

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Abstract

We propose a new method both for automatically creating non-uniform, context-dependent HMM topologies, and selecting the number of mixture components based on the Variational Bayesian (VB) approach. Although the Maximum Likelihood (ML) criterion is generally used to create HMM topologies, it has an over-fitting problem. Recently, to avoid this problem, the VB approach has been applied to create acoustic models for speech recognition. We introduce the VB approach to the Successive State Splitting (SSS) algorithm, which can create both contextual and temporal variations for HMMs. Experimental results indicate that the proposed method can automatically create a more efficient model than the original method. We evaluated a method to increase the number of mixture components by using the VB approach and considering temporal structures. The VB approach obtained almost the same performance as the smaller number of mixture components in comparison with that obtained by using ML-based methods.