A study of on-line quasi-Bayes adaptation for CDHMM-based speech recognition

We present a framework of quasi-Bayes (QB) learning of the parameters of the continuous density hidden Markov model (CDHMM) with Gaussian mixture state observation densities. Based on the theory of recursive Bayesian inference, the QB algorithm is designed to incrementally update the hyperparameters on the approximate posterior distribution and the CDHMM parameters simultaneously. By further introducing a simple forgetting mechanism to adjust the contribution of previously observed sample utterances, the algorithm is adaptive in nature and capable of performing an on-line adaptive learning using only the current sample utterance. It can thus be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, and transducers. As an example, the QB learning framework is applied to on-line speaker adaptation and its viability is confirmed in a series of comparative experiments using a 26-letter English alphabet vocabulary