9th Annual Conference of the International Speech Communication Association

Brisbane, Australia
September 22-26, 2008

Bayesian Context Clustering Using Cross Valid Prior Distribution for HMM-Based Speech Recognition

Kei Hashimoto, Heiga Zen, Yoshihiko Nankaku, Akinobu Lee, Keiichi Tokuda

Nagoya Institute of Technology, Japan

This paper proposes a prior distribution determination technique using cross validation for speech recognition based on the Bayesian approach. The Bayesian method is a statistical technique for estimating reliable predictive distributions by marginalizing model parameters and its approximate version, the variational Bayesian method has been applied to HMM-based speech recognition. Since prior distributions representing prior information about model parameters affect the posterior distributions and model selection, the determination of prior distributions is an important problem. However, it has not been thoroughly investigate in speech recognition. The proposed method can determine reliable prior distributions without tuning parameters and select an appropriate model structure dependently on the amount of training data. Continuous phoneme recognition experiments show that the proposed method achieved a higher performance than the conventional methods.

Full Paper

Bibliographic reference.  Hashimoto, Kei / Zen, Heiga / Nankaku, Yoshihiko / Lee, Akinobu / Tokuda, Keiichi (2008): "Bayesian context clustering using cross valid prior distribution for HMM-based speech recognition", In INTERSPEECH-2008, 936-939.