SEMI-SUPERVISED LEARNING BASED CHINESE DIALECT IDENTIFICATION

被引：0
|
作者：
Gu Mingliang [1 ,2 ]
Xia Yuguo [2 ]
Yang Yiming [2 ]
机构：
[1] Xuzhou Normal Univ, Sch Phys & Elect Engn, Xuzhou 221116, Peoples R China
[2] Neural & Congnit Language Engn, Jiangsu Key Lab, Xuzhou 221116, Peoples R China
来源：
ICSP: 2008 9TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, VOLS 1-5, PROCEEDINGS | 2008年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The main contribution of this paper is to present the novel information fusing framework. The shifted delta cepstra (SDC) feature and prosodic feature are firstly used to train two base classifiers respectively. Then co-training algorithm presented in semi-supervised learning is employed to improve Chinese dialect identification accuracy. Four Chinese dialects is tested with this system. The experimental results showed that the proposed system outperformed the original GMM based system.
引用
收藏
页码：1609 / +
页数：2
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