Text-independent speaker recognition using probabilistic SVM with GMM adjustment

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 20033 speaker recognition evaluation database. Results on text-independent speaker identification and verification are provided to demonstrate the effectiveness of such systems.