Multigrained Model Adaptation With Map and Reference Speaker Weighting For Text Independent Speaker Verification

When traditional maximum a posteriori (MAP) adaptation is used to adapt a universal background model (UBM), some model components with little or no enrollment data would remain unchanged in the derived speaker model. These model components would have weak discriminative capability over the background model, and would impair subsequent verification performance. In this paper, we present a new speaker adaptation method which combines MAP and reference speaker weighting (RSW) adaptation in a hierarchical, multigrained mode. It enables all model components to be updated in a way that strikes a good balance between model complexity and available data. The experimental results of NIST speaker recognition evaluation confirmed the effective performance increase with this new method compared with using MAP or RSW adaptation techniques alone