Sequential Model Adaptation For Speaker Verification

GMM-UBM-based speaker verification heavily relies on well-trained UBMs. In practice, it is not often easy to obtain a UBM that fully matches the acoustic channel in operation. In a previous study, we proposed to address this problem by a novel sequential UBM adaptation approach based on MAP. This work extends the study by applying the sequential approach to speaker model adaptation. In addition, we investigate a new feature-space sequential adaptation approach based on feature MAP linear regression (fMAPLR) and compare it with the previously proposed model-space MAP approach. We find that these two approaches are complementary and can be combined to deliver additional performance gains. The experiments conducted on a time-varying speech database demonstrate that the proposed MAP-fMAPLR approach leads to significant EER reduction with two mismatched UBMs (25% and 39% respectively).