Speaker identification and verification of noisy speech using multitaper MFCC and Gaussian Mixture models

The two major applications of speaker recognition applications are speaker verification and speaker identification. But in most of the cases the signal is corrupted with background interferences such as noise and echo. This paper proposes the method of speaker recognition and identification after the noise separation. Support Vector Machine(SVM) classification based signal separation is adopted here. MFCC and Multitaper MFCC are used for feature extraction. Despite having low bias, MFCC has large variance. One promising technique for reducing the variance is to replace Hamming windowed spectrum with a multi-taper spectrum estimate. Gaussian Mixture models along with Universal Background Model(UBM) is used for speaker verification and identification tasks.