On-line adaptive learning of CDHMM parameters based on multiple-stream prior evolution and posterior pooling

Based on the concept of multiple-stream prior evolution and posterior pooling, we propose a new incremental adaptive Bayesian learning framework for e cient on-line adaptation of the continuous density hidden Markov model (CDHMM) parameters. As a rst step, we apply the a ne transformations to the mean vectors of CDHMMs to control the evolution of their prior distribution. This new stream of prior distribution can be combined with another stream of prior distribution evolved without any constraints applied. In a series of comparative experiments on the task of continuous Mandarin speech recognition, we show that the new adaptation algorithm achieves a similar fast-adaptation performance as that of incremental MLLR (maximum likelihood linear regression) in the case of small amount of adaptation data, while maintains the good asymptotic convergence property as that of our previously proposed quasi-Bayes adaptation algorithms.