关键词: 汉语方言辨识, 主动学习, 支持矢量机, 多样性测度

Abstract: In order to solve the problem of the redundant training samples in dialect identification system, an approach for Chinese dialect identification fusing diversity measure is proposed. Firstly, the uncertain samples are chosen by SVM classifier, then according to the distribution of these samples, the uncertain samples with diversity are selected and the new training set including these distinctive samples is constructed after several iterations. Finally, SVM is reused to make the decision. Experimental results indicate that, compared with the traditional active method, the proposed approach effectively overcomes the redundancy of the samples and the number of manually annotated samples reduces 50% under the same condition of recognition accuracy.

Key words: Chinese dialect identification, active learning, Support Vector Machine（SVM）, diversity measure