Speaker verification normalization sequence kernel based on Gaussian mixture model super-vector and Bhattacharyya distance

Abstract

Due to the accessibility and economy of human speech, speaker verification has become the research hotspot in the field of biometric authentication. A novel normalization sequence kernel based on Bhattacharyya distance clustering and within class covariance normalization was proposed in this paper. In this kernel, the high computation complexity and channel interference susceptibility that commonly exist in speaker verification could be restrained. In our method, we calculated the Bhattacharyya distance between pairs of Gaussian mixture models first. And then, a clustering algorithm was designed according to Gaussian mixture model’s Bhattacharyya distance to obtain clustering center models. Maximum a posteriori was applied on these clustering center models to generate super-vectors immediately following. The sequence kernel was generated based on Bhattacharyya distance transformation and super-vectors. Finally, within class covariance normalization was utilized to restrain the channel distortion in kernel space. We adopted support vector machine as classifier to decide the target speaker. The experiment results on TIMIT corpus and NIST 2008 SRE showed that our proposed kernel has superior recognition accuracy and better robustness.