Multi-taper MFCC features for speaker verification using I-vectors

This paper studies the low-variance multi-taper mel-frequency cepstral coefficient (MFCC) features in the state-of-the-art speaker verification. The MFCC features are usually computed using a Hamming-windowed DFT spectrum. Windowing reduces the bias of the spectrum but variance remains high. Recently, low-variance multi-taper MFCC features were studied in speaker verification with promising preliminary results on the NIST 2002 SRE data using a simple GMM-UBM recognizer. In this study our goal is to validate those findings using a up-to-date i-vector classifier on the latest NIST 2010 SRE data. Our experiment on the telephone (det5) and microphone speech (det1, det2, det3 and det4) indicate that the multi-taper approaches perform better than the conventional Hamming window technique.