Speaker Verification Using SVM Kernel with GMM-Supervector Based on the Mahalanobis Distance

In this paper, we propose speaker verification method using Support Vector Machine (SVM) kernel with Gaussian Mixture Model (GMM) supervector based on the Mahalanobis distance, The proposed GMM supervector SVM kernel method is combined GMM with SVM, The GMM supervectors are generated by GMM parameters of speaker and other speaker utterances, A speaker verification threshold of GMM supervectors is decided by SVM kernel based on Mahalanobis distance to improve speaker verification accuracy. The experimental results for text independent speaker verification using 20 speakers demonstrates the performance of the proposed method compared to GMM, SVM, GMM supervector SVM kernel based on Kullback-Leibler (KL) divergence, and GMM supervector SVM kernel based on Bhattacharyya distance.