Discriminative Transformation for Sufficient Adaptation in Text-Independent Speaker Verification

In conventional Gaussian Mixture Model – Universal Background Model (GMM-UBM) text-independent speaker verification applications, the discriminability between speaker models and the universal background model (UBM) is crucial to system’s performance. In this paper, we present a method based on heteroscedastic linear discriminant analysis (HLDA) that can enhance the discriminability between speaker models and UBM. This technique aims to discriminate the individual Gaussian distributions of the feature space. After the discriminative transformation, the overlapped parts of Gaussian distributions can be reduced. As a result, some Gaussian components of a target speaker model can be adapted more sufficiently during Maximum a Posteriori (MAP) adaptation, and these components will have more discriminative capability over the UBM. Results are presented on NIST 2004 Speaker Recognition data corpora where it is shown that this method provides significant performance improvements over the baseline system.