Abstract: GMM and SVM have a good complementation on the modeling and recognition performance．Therefore,GMM-MMI-SVM has become a mainstream research method in language recognition．However,SVM only employs some special samples in the training samples,i.e．support vector,but doesn’t use all samples．This affects further improvement of system’s recognition performance．In order to solve this problem,an novel classification algorithm based on Kernel Fisher Discriminant(KFD) was proposed in this paper,called GMM-MMI-KFD．The core idea is the substitution of SVM with KFD,Extracting eigenvector sequence from voice segment,and then inputing them into GMM-MMI and GMM-KFD classifiers respectively,which judge them．Compared to SVM,KFD gets more emphasis on the characteristic of nonlinear distribution of voice data．Meanwhile,it can maximize between-class space and minimize within-class space after the projection of samples onto high-dimensional space．The experimental data shows that the GMM-MMI-KFD Classifier has higher recognition rate in language recognition.

Key words: Language recognition,Kernel fisher discriminant,Classifier fusion,SVM,GMM-MMI