A text-independent speaker verification system using support vector machines classifier

In the recent years the technology for speaker verification or call authentication has received an increasing amount of attention in IVR industry. However due to the complexity of speaker information embedded in the speech signals the current technology still can not produce the verification accuracy to meet the requirement for some applications. In this paper we introduce a new pattern classification approach, support vector machines (SVM) for the text-independent speaker verification. The SVM is a new way of statistical learning based on a principle of structural risk minimisation. In the paper various evaluation results for the SVM verification system are presented and a comparison with a baseline GMM approach is also given. The results demonstrate that the SVM approach perform much better than the GMM approach. On the same training and testing data set the SVM approach gives an EER 1.2% versus 3.9% EER from the GMM approach.