Using LR-based discriminant kernel methods with applications to speaker verification

Abstract

Kernel methods are powerful techniques that have been widely discussed and successfully applied to pattern recognition problems. Kernel-based speaker verification has also been developed to use the concept of sequence kernel that is able to deal with variable-length patterns such as speech. However, constructing a proper kernel cleverly tied in with speaker verification is still an issue. In this paper, we propose the new defined kernels derived by the Likelihood Ratio (LR) test, named the LR-based kernels, in attempts to integrate kernel methods with the LR-based speaker verification framework tightly and intuitively while an LR is embedded in the kernel function. The proposed kernels have two advantages over existing methods. The first is that they can compute the kernel function without needing to represent the variable-length speech as a fixed-dimension vector in advance. The second is that they have a trainable mechanism in the kernel computation using the Multiple Kernel Learning (MKL) algorithm. Our experimental results show that the proposed methods outperform conventional speaker verification approaches.