We improve upon the current Hidden Markov Model (HMM) techniques for speaker recognition by using the means of Gaussian mixture components of keyword HMM states in a support vector machine (SVM) classifier. We achieve an 11% improvement over the traditional keyword HMM approach on SRE06 for the 8 conversation task, using the original set of keywords. Using an expanded set of keywords, we achieve a 4.3% EER standalone on SRE06, and a 2.6% EER in combination with a word-conditioned phone N-grams system, a GMM-based system, and the traditional keyword HMM system on SRE05+06. The latter result improves on our previous best.
Bibliographic reference. Lei, Howard / Mirghafori, Nikki (2007): "Word-conditioned HMM supervectors for speaker recognition", In INTERSPEECH-2007, 746-749.