Speaker verification with the mixture of Gaussian factor analysis based representation

Abstract

This paper presents a generalized i-vector representation framework using the mixture of Gaussian (MoG) factor analysis for speaker verification. Conventionally, a single standard factor analysis is adopted to generate a low rank total variability subspace where the mean supervector is assumed to be Gaussian distributed. The energy that can't be represented by the low rank space is modeled by a single multivariate Gaussian. However, due to the sparsity of the frame level posterior probability and the short duration characteristics, some dimensions of the first-order statistics may not be Gaussian distributed. Therefore, we replace the single Gaussian with a mixture of Gaussians to better represent the residual energy. Experimental results on the NIST SRE 2010 condition 5 female task and the RSR 2015 part 1 female task show that the MoG i-vector outperforms the i-vector baseline by more than 10% relatively for both text independent and text dependent speaker verification tasks, respectively.