A GMM supervector Kernel with the Bhattacharyya distance for SVM based speaker recognition

Gaussian mixture model (GMM) supervector is one of the effective techniques in text independent speaker recognition. In our previous work, we introduce the GMM-UBM mean interval (GUMI) concept based on the Bhattacharyya distance. Subsequently GUMI kernel was successfully used in conjunction with support vector machine (SVM) for speaker recognition. Besides the first order statistics, it is generally believed that speaker cues are also partly conveyed by second order statistics. In this paper, we extend the Bhattacharyya-based SVM kernel by constructing the supervector with the mean statistical vector and the covariance statistical vector. Comparing with the Kullback-Leibler (KL) kernel, we demonstrate the effectiveness of the new kernel on the 2006 National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) dataset.