Support vector machine based Chinese dialect identification
Changshui Zhang · Computer Engineering and Applications Journal · 2007
Statistical learning theory has proved that support vector machine has higher classification ability and higher generalization.However,it is not directly used to Chinese dialect identification,as the speech is a dynamic model.This paper resolves this problem successfully using the global features consisted with the likelihood of Gaussian mixture model and language model,and enhances the discrimination of the system greatly.The experimental results show that SVM based classifier can raise the rate of correct identification about 20% and 4% respectively compared with traditional discriminative classifier and Artificial Neural Network(ANN).