Language Identification by Hierarchical Support Vector Machine
The GLDS-SVM has achieved excellent performance on language identification task.Based on the analysis of it,a hierarchical SVM method is proposed in this paper.It splits the training utterances into different duration sets,and selects data from the short-time duration set by the SVM models trained using the long-time duration set.Furthermore,we also explore two complementary feature sets based on a co-training style approach,so the performance can be further improved by the fusion of systems.Experimental results on NIST 2003 LRE dataset have demonstrated the effectiveness of the proposed approach.More specifically,the EER is reduced from 6.3% to 4.5%.