Speaker Verification Using Line Spectrum Frequency, Formant, and Support Vector Machine

Abstract

Speaker verification is desirable widely in many speech related applications, such as automatic telephone banking and biometric security system. This paper proposes an application of line spectrum frequency (LSF), formant, and support vector machine (SVM) to develop an algorithm of text-dependent speaker verification system. First, LSF and formant are extracted from the voiced password provided by the user. Then the proposed algorithm will make use of SVM to train the speaker characteristics from these speaker features and finally generate a claimed speaker model to discriminate between the speaker and other impostors. Experiments were conducted on the real speech signals and shown the performance of the proposed algorithm yields an equal error rate (EER) of 2.12% with 8-order LSFs and formant information. In addition, both of the false acceptance rate (FAR) and the false rejection rate (FRR) are also improved remarkably.