Speaker Identification Using Discriminative Learning of Large Margin GMM

Gaussian mixture models (GMM) have been widely and successfully used in speaker recognition during the last decades. They are generally trained using the generative criterion of maximum likelihood estimation. In an earlier work, we proposed an algorithm for discriminative training of GMM with diagonal covariances under a large margin criterion. In this paper, we present a new version of this algorithm which has the major advantage of being computationally highly efficient, thus well suited to handle large scale databases. We evaluate our fast algorithm in a Symmetrical Factor Analysis compensation scheme. We carry out a full NIST speaker identification task using NIST-SRE’2006 data. The results show that our system outperforms the traditional discriminative approach of SVM-GMM supervectors. A 3.5% speaker identification rate improvement is achieved.