Fusing generative and discriminative models for Chinese dialect identification

This paper presents a fusing framework of discriminative and generative models for Chinese dialect identification. The generative models are employed to produce language feature vectors and the discriminative models are used to make classification. Four Chinese dialects is tested with this system. The experimental results showed that the proposed system outperformed the GMM based system. Meanwhile the SVM based discriminative methods has more powerful discriminative ability than ANN based one.