Efficient parameterization for Automatic speaker recognition using Support Vector Machines

Recent advances in the field of speaker recognition have proved to highly outperform algorithms. However this performance degrades when limited data are presented. This paper presents examples on how SVM can improve speaker recognition. The main contribution in this approach is the use of new low-dimensional vectors when training data are limited. We show how different kernels function of Support Vector Machines (SVM) can be used to deal a new approach for speaker recognition system. We achieved remarkable results using TIMIT database.