Discriminating speakers by their voices — A fusion based approach

Abstract

The task of Speaker Discrimination (SD) consists in checking whether two speech segments belong to the same speaker or not. In this research field, it is often difficult to decide what could be the best classifier in terms of accuracy and robustness. For that purpose, we have implemented 9 classifiers: Support Vector Machines, Linear Discriminant Analysis, Multi-Layer Perceptron, Generalized Linear Model, Self Organizing Map, Adaboost, Second Order Statistical Measures, Linear Regression and Gaussian Mixture Models. Furthermore, a new fusion approach is proposed and experimented in speaker discrimination. Several experiments of speaker discrimination were conducted on Hub4 Broadcast-News with relatively short segments. The obtained results have shown that the best classifier is the SVM and that the proposed fusion approach is quite interesting since it provided the best performances at all.