Fusion of a Novel Volterra-Wiener Filter Based Nonlinear Residual Phase and MFCC for Speaker Verification

Abstract

This paper investigates the complementary nature of the speaker-specific information present in the Volterra-Wiener filter residual (VWFR) phase of speech signal in comparison with the information present in conventional Mel Frequency Cepstral Coefficients (MFCC) and Teager Energy Operator (TEO) phase. The feature set is derived from residual phase extracted from the output of nonlinear filter designed using Volterra-Weiner series exploiting higher order linear as well as nonlinear relationships hidden in the sequence of samples of speech signal. The proposed feature set is being used to conduct Speaker Verification (SV) experiments on NIST SRE 2002 database using state-of-the-art GMM-UBM system. The score-level fusion of proposed feature set with MFCC gives an EER of 6.05% as compared to EER of 8.9% with MFCC alone. EER of 8.83% is obtained for TEO phase in fusion with MFCC, indicating that residual phase from proposed nonlinear filtering approach contain complementary speaker-specific information.