Speaker Verification Using LR-based Composite Sequence Kernel for Improving the Characterization of the Alternative Hypothesis

Abstract

The likelihood ratio (LR)-based speaker verification is usually difficult to characterize the alternative hypothesis precisely. To better characterize the alternative hypothesis, we propose to incorporate two effective speaker verification approaches based on weighted geometric combination (WGC) and weighted arithmetic combination (WAC) into the support vector machine (SVM) via a new sequence kernel function, named the LR-based composite sequence kernel. This new kernel can be regarded as a unified framework for characterizing the alternative hypothesis by virtue of the complementary information that the WGC and WAC approaches can contribute. Our experiment results show that the proposed sequence kernel method outperforms the conventional speaker verification approaches.