Automatic detection and evaluation of Erhua in the Putonghua proficiency test  

作　　者：ZHANG Long LI Haifeng MA Lin WANG Jianhua 

机构地区：[1]College of Computer Science and Information Engineering,Harbin Normal University [2]School of Computer Science and Technology,Harbin Institute of Technology

出　　处：《Chinese Journal of Acoustics》2014年第1期83-96,共14页声学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(41071262,61171186);the Natural Science Foundation of Heilongjiang Province of China(F201321)

摘　　要：An automatic detection and evaluation method of the Erhua （also called r-retroflexion or retroflex suffixation） in the Putonghua proficiency test （PSC） is proposed. Based on the framework of the computer assisted pronunciation evaluation system, the present authors made an in-depth analysis of phonologic rules and acoustic characteristics of the Erhua, and solved the detection and evaluation of the Erhua as a typical classification problem. Then more rep- resentative acoustic features were selected and a variety of different classification algorithms were used. The results showed that the boosting classification and regression tree （Boosting CART） could make full use of the characteristics of the Erhua, and the classification accuracy was 92.41%. Based on further analysis of the acoustic feature group, it was found that formant, pronunciation confidence and duration were the most important clues of the Erhua, and these clues could effectively realize the automatic detection and evaluation of the Erhua.An automatic detection and evaluation method of the Erhua （also called r-retroflexion or retroflex suffixation） in the Putonghua proficiency test （PSC） is proposed. Based on the framework of the computer assisted pronunciation evaluation system, the present authors made an in-depth analysis of phonologic rules and acoustic characteristics of the Erhua, and solved the detection and evaluation of the Erhua as a typical classification problem. Then more rep- resentative acoustic features were selected and a variety of different classification algorithms were used. The results showed that the boosting classification and regression tree （Boosting CART） could make full use of the characteristics of the Erhua, and the classification accuracy was 92.41%. Based on further analysis of the acoustic feature group, it was found that formant, pronunciation confidence and duration were the most important clues of the Erhua, and these clues could effectively realize the automatic detection and evaluation of the Erhua.

分 类 号：H102[语言文字—汉语]