Abstract

We extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models with Gaussian mixture state observation densities in which all mean vectors are assumed to be correlated and have a joint prior distribution. A successive approximation algorithm is proposed to implement the correlated mean vectors' updating. As an example, by applying the method to on-line speaker adaptation application, the algorithm is experimentally shown to be asymptotic convergent as well as being able to enhance the efficiency and the effectiveness of the Bayes learning by taking into account the correlation information between different models. The technique can be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, transducers, environments and so on.

Citations