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Text-independent speaker recognition using probabilistic SVM with GMM adjustment
International Conference on Natural Language Processing and Knowledge Engineering, 2003. Proceedings. 2003
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There are two most popular techniques in pattern recognition, discriminative classifiers and generative model classifiers. Combining them together could improve the performance of the recognition system. This paper presents a novel method for text-independent speaker recognition. This system uses the output of the Gaussian mixture model to adjust the probabilistic output of the support vector machine. The new probabilistic SVM/GMM model based speaker recognition system is tested on the NIST
doi:10.1109/nlpke.2003.1275919
fatcat:g2thng5shve7zkpapqumghee44