Using Information Theoretic Vector Quantization for Inverted MFCC based Speaker Verification

Over the recent years different versions the GMM classifier combined with the MFCC features have been established as speaker verification benchmarks. Although highly efficient, these systems suffer from computational complexity and occasional convergence problems. In this study a search of alternative classification and feature extraction methods of similar classification efficiency but overcoming some of the problems of the classical methods was undertaken. Preliminary results obtained for two different classification methods: the classical GMM and the ITVQ and three different feature extraction methods: MFCC, IMFCC and the MFCC/IMFCC fusion are presented. The ITVQ did not show better results compare to the classical GMM classifier, however the EER increase in case for the ITVQ was only by 0.2%. The best feature extraction method was proven to be the MFCC/IMFCC fusion. Both the MFCC/IMFCC fusion and the IMFCC outperformed the classical MFCC method.