Bayesian context clustering using cross valid prior distribution for HMM-based speech recognition
Kei Hashimoto, Heiga Zen, Yoshihiko Nankaku, Akinobu Lee, Keiichi Tokuda · 2008
This paper proposes a prior distribution determination tech-nique using cross validation for speech recognition based on the Bayesian approach. The Bayesian method is a statisti-cal 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 represent-ing prior information about model parameters affect the pos-terior distributions and model selection, the determination of prior distributions is an important problem. However, it has not been thoroughly investigate in speech recognition. The pro-posed method can determine reliable prior distributions with-out tuning parameters and select an appropriate model struc-ture dependently on the amount of training data. Continu-ous phoneme recognition experiments show that the proposed method achieved a higher performance than the conventional methods. Index Terms: variational Bayes, cross validation, context clus-tering, continuous phoneme recognition