VVGP features for speaker verification using i-vector framework

Spectral parameters alone, especially the Mel-Frequency Cepstral Coefficients (MFCCs) and perceptual linear prediction (PLP) coefficients, have shown good performance in speaker recognition. However, the cepstrum carries only linear information. In this paper, we study the performance of the Variable Variance Gaussian Parameter (VVGP) in a state-of-the-art i-vector speaker verification system. Experimental results on the Ynoguti 2 database indicate that VVGP features is complementary to MFCCs and can improve recognition accuracy.