Abstract In the text-independent speaker verification research, the probability distribution against the universal background model (PD-UBM) is calculated. And the score of each UBM Gaussian mixture is adopted as the input feature of the support vector machine (SVM) during the training and testing process. The proposed PD-UBM algorithm with linear kernel function can obtain the same or better performance as the generalized linear discriminant sequence (GLDS) kernel system. Furthermore, if the scores of the Gaussian mixture models (GMM-UBM), the GLDS and the PD-UBM are combined, the significant improvement of the system can be achieved. In 2006, on NIST 1conv4w-1conv4w speaker recognition evaluation (SRE) corpus, the fusion system obtained 25% relative improvement equal error rate (ERR) of over the GMM-UBM system.
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2008, Vol. 21 
