Applications of Dirichlet Process Mixtures to speaker adaptation

Balancing unique acoustic characteristics of a speaker such as identity and accent, with general acoustic behavior that describes phoneme identity, is one of the great challenges in applying nonparametric Bayesian approaches to speaker adaptation. The Dirichlet Process Mixture (DPM) is a relatively new model that provides an elegant framework in which individual characteristics can be balanced with aggregate behavior without diluting the quality of the individual models. Unlike Gaussian Mixture models (GMMs), which tend to smear multimodal behavior through averaging, the DPM model attempts to preserve unique behaviors through use of an infinite mixture model. In this paper, we present some exploratory research on applying these models to the acoustic modeling component of the speaker adaptation problem. DPM based models are shown to provide up to 10% reduction in WER over maximum likelihood linear regression (MLLR) on a speaker adaptation task based on the Resource Management database.