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智慧网络下MOOC与计算机网络课程协同教学的整合
  • ISSN号:1672-5913
  • 期刊名称:《计算机教育》
  • 时间:0
  • 分类:TP274[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置] H102[语言文字—汉语]
  • 作者机构:[1]College of Computer Science and Information Engineering, Harbin Normal University Harbin 150025, [2]School of Computer Science and Technology, Harbin Institute of Technology Harbin 150001
  • 相关基金:This work was supported by the National Natural Science Foundation of China (41071262, 61171186) and the Natural Science Foundation of Heilongjiang Province of China (F201321).
中文摘要:

An automatic detection and evaluation method of the Erhua(also called r-retroflexion or retrofex 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 representative acoustic features were selected and a variety of difierent 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.

英文摘要:

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.

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期刊信息
  • 《计算机教育》
  • 主管单位:教育部
  • 主办单位:清华大学
  • 主编:奚春燕
  • 地址:北京市海淀区双清路清华大学学研大厦B座606室
  • 邮编:100084
  • 邮箱:jsjjy@vip.163.com
  • 电话:010-62770175-3402-3406
  • 国际标准刊号:ISSN:1672-5913
  • 国内统一刊号:ISSN:11-5006/TP
  • 邮发代号:80-171
  • 获奖情况:
  • 国内外数据库收录:
  • 被引量:26095