Joint map adaptation of feature transformation and Gaussian Mixture Model for speaker recognition

This paper extends our previous work on feature transformation-based support vector machines for speaker recognition by proposing a joint MAP adaptation of feature transformation (FT) and Gaussian Mixture Models (GMM) parameters. In the new approach, the prior probability density functions (PDFs) of FT and GMM parameters are jointly estimated using the background data under the maximum likelihood criteria. In this way, we derive a generic prior GMM that is more compact than the Universal Background Model due to the reduction of speaker variations. With the prior PDFs, we construct a supervector to characterize a speaker using FT and GMM parameters. We conducted experiments on NIST 2006 Speaker Recognition Evaluation (SRE06) data set. The results validated the effectiveness of the joint MAP adaptation approach.