Robust text-independent speaker identification using multiple subband-classifiers in colored noise environment

Abstract

A new approach for robust speaker identification using multiple classifiers in the subband domain is presented. The proposed system uses weights calculated from the energy in each subband of the speech signal to combine a set of likelihoods provided by GMM subband-classifiers. Experiments show the effectiveness of the proposed scheme as compared to other techniques.