Rapid online adaptation based on transformation space model evolution

This paper presents a new approach to online linear regression adaptation of continuous density hidden Markov models based on transformation space model (TSM) evolution. The TSM which characterizes the a priori knowledge of the training speakers associated with maximum likelihood linear regression matrix parameters is effectively described in terms of the latent variable models such as the factor analysis or probabilistic principal component analysis. The TSM provides various sources of information such as the correlation information, the prior distribution, and the prior knowledge of the regression parameters that are very useful for rapid adaptation. The quasi-Bayes estimation algorithm is formulated to incrementally update the hyperparameters of the TSM and regression matrices simultaneously. The proposed TSM evolution is a general framework with batch TSM adaptation as a special case. Experiments on supervised speaker adaptation demonstrate that the proposed approach is more effective compared with the conventional quasi-Bayes linear regression technique when a small amount of adaptation data is available.