2001: A Speaker Odyssey - The Speaker Recognition Workshop
June 18-22, 2001
Current best performing speaker recognition algorithms are based on Gaussian Mixture Models (GMM). Their results are not satisfactory for all experimental conditions, especially for the mismatched (train/test) conditions. Support Vector Machine is a new and very promising technique in statistical learning theory. Recently, this technique produced very interesting results in image processing and for the fusion of experts in biometric authentication. In this paper we address the issue of using the Support Vector Learning technique in combination with the currently well performing GMM, in order to improve speaker verification results. The results are compared to the classical Log-Likelihood Ratio (LLR) technique on a sub-set of NIST 1999 evaluation database which is a part of the Switchboard corpus. The influence of the hnorm normalization is also studied. In all the cases, the proposed systems using SVM outperform the classical LLR based systems.
Full Paper Presentation
Bibliographic reference. Kharroubi, Jamal / Petrovska-Delacrétaz, Dijana / Chollet, Gérard (2001): "Text-independent speaker verification using support vector machines", In ODYSSEY-2001, 51-54.