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Combination of likelihood scores using linear and SVM approaches for text-independent speaker verification

In this paper, the text-independent speaker recognition system based on the adapted GMMs was established, and the speaker-independent background model and speaker-dependent models of cohort speaker sets were used to normalize the likelihood score. The approaches to combine likelihood scores using linear and SVM (support vector machine) method in score domain was proposed. The speaker verification experiments over telephone channels showed that based on the likelihood ratio of adapted GMMs system, combination of likelihood scores can improve the verification performance of baseline system using universal background model (UBM). Specially, the approach of score combination using SVM achieved the best performance.