The recently introduced mean Hilbert envelope coefficients (MHEC) have been shown to be an effective alternative to MFCCs for robust speaker identification under noisy and reverberant conditions in relatively small tasks. In this study, we investigate the effectiveness of these acoustic features in the context of a state-of-the-art speaker recognition system. The i-vectors are used to represent the acoustic space of speakers, while modeling is performed via probabilistic linear discriminant analysis (PLDA). We report speaker verification performance on the NIST SRE-2010 extended telephone and microphone trials for both female and male genders. Experimental results confirm consistent superiority of MHECs to traditional MFCCs within i-vector speaker verification, particularly under microphone and telephone training-test mismatch conditions. In addition, fusion of subsystems trained with the individual front-ends proves that the two acoustic features (i.e., MHEC and MFCC) provide complimentary information for recognizing speakers.

Index Terms: Mean Hilbert Envelope Coefficients (MHEC), mismatch conditions, NIST SRE, speaker recognition