Abstract：The poor performance of speaker verification system results from the channel mismatch and the lack of distinction between statistical models. A text-independent speaker verification method is proposed which combines the channel compensation based on factor analysis and the discriminative support vector machine (SVM) model. Gaussian mixture model (GMM) is used to make the speech parameter clustered and ascended, then the channel information of GMM mean super-vectors is wiped off by using factor analysis. The parameters, which are used as inputting parameters, are employed for the construction of SVM speaker verification system. The proposed method solves the problems of large samples, dimension raising and channel mismatch effectively when SVM is used for the text-independent speaker verification. Experimental results on NIST 06 male speaker corpus show that the proposed method improves system performance. Compared with the baseline system Gaussian mixture model-universal background model (GMM-UBM), GMM-SVM without channel compensation, the system improves the equal error rate (EER) more than 50%.Compared with the system factor analysis (FA)-GMM-UBM which uses channel compensation based on factor analysis without discriminative models, it also gets the improvement of EER by 15.8%.
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