On-line Bayes adaptation of SCHMM parameters for speech recognition

Abstract

On-line adaptation of semi-continuous (or tied mixture) hidden Markov model (SCHMM) is studied. A theoretical formulation of the segmental quasi-Bayes learning of the mixture coefficients in SCHMM for speech recognition is presented. The practical issues related to the use of this algorithm for on-line speaker adaptation are addressed. A pragmatic on-line adaptation approach to combine the long-term adaptation of the mixture coefficients and the short-term adaptation of the mean vectors of the Gaussian mixture components are also proposed. The viability of these techniques are confirmed in a series of comparative experiments using a 26-word English alphabet vocabulary.