A novel speaker identification algorithm using classifiers fusion
In this paper, a novel speaker identification technique using the Dempster-Shafer evidence theory is discussed. The objective is to fuse the complementary information present from different classifiers into a single decision. Here, we use a decreasing function of the distance (of the classifiers) as the belief function. We show that a combined classifier based on the Dempster-Shafer theory outperforms the individual LPCC and MFCC classifiers when used individually.