Discriminative training of weighted polynomial vector for acoustic language recognition

In this paper, we propose a discriminative method for the acoustic feature based language recognizer, which is a modification of the polynomial expansion in generalized linear discriminant sequence (GLDS) kernel. It is inspired by the Gaussian mixture model-support vector machine (GMM-SVM) system which has been successfully used in both speaker and language recognition. Because of the restriction of calculations in our method, it is nearly impossible to stack component dependent polynomial expansion vectors as GM-MSVM system does. Thus we introduce a set of language dependent weights to fuse these expansion vectors and utilize maximum mutual information (MMI) criterion and logistic regression to estimate the model parameters. Finally, we evaluate our method on the close-set, 30 seconds test condition of NIST LRE 2007 and up to 30% relative improvement can be achieved comparing to the baseline GLDS system.