Text-independent speaker recognition using probabilistic SVM with GMM adjustment
Fenglei Hou, Bingxi Wang · 2004
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. We present 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 2003 speaker recognition evaluation database. Results on text-independent speaker identification and verification are provided to demonstrate the effectiveness of such systems.