Improving Viterbi Bayesian predictive classification via sequential Bayesian learning in robust speech recognition

We extend our previously proposed Viterbi Bayesian predictive classification (VBPC) algorithm to accommodate a new class of prior probability density function (PDF) for continuous density hidden Markov model (CDHMM) based robust speech recognition. The initial prior PDF of CDHMM is assumed to be a finite mixture of natural conjugate prior PDF´s of its complete-data density. With the new observation data, the true posterior PDF is approximated by the same type of finite mixture PDF´s which retain the required most significant terms in the true posterior density according to their contribution to the corresponding predictive density. Then the updated mixture PDF is used to improve the VBPC performance. The experimental results on a speaker-independent recognition task of isolated Japanese digits confirm the viability and the usefulness of the proposed technique