Robust Speaker Verification Using a New Front End Based on Multitaper and Gammatone Filters.

In this paper we present a novel feature extraction algorithm based on Multitaper windows and Gammatone filters for robust speaker verification systems in mismatched noisy conditions encountered in forensic area. The idea is to couple the advantage of the low-variance multitaper short term spectral estimators with the acoustic robustness of the auditory Gammatone filterbanks. Experimental results on the TIMIT corpus, with mismatched environment and low environmental signal to noise ratios (SNR) levels, show that the proposed Multitaper Gammatone Cepstral Coefficient (MGCC) features outperform largely the conventional Mel Frequency Cepstral Coefficients (MFCC) features. Furthermore, and interestingly the proposed features outperforms at almost all the operating signal to noise ratios the recently proposed auditory hearing inspired Gammatone Frequency Cepstral Coefficient (GFCC) feature for white, babble and factory noises using both the GMM-UBM de facto standard and the state-of -the art I-vector speaker verification systems.