8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Word-Conditioned HMM Supervectors for Speaker Recognition

Howard Lei, Nikki Mirghafori


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.

Full Paper

Bibliographic reference.  Lei, Howard / Mirghafori, Nikki (2007): "Word-conditioned HMM supervectors for speaker recognition", In INTERSPEECH-2007, 746-749.