A SVM/HMM system for speaker recognition

A framework for combining support vector machines with hidden Markov models (HMM) is given. A HMM is used with a Viterbi alignment to generate a set of subsequences of feature vectors. Each subsequence is then scored using a support vector machine sequence kernel. Experiments are performed for both text-independent and text-prompted speaker recognition tasks. Results show that the method can dramatically reduce error rates over a support vector machine (SVM) only system.