Development of an Automatic Evaluation System of Reading-Aloud and Shadowing Performances by Japanese EFL Learners Using the Latest Speech Technology

利用最新语音技术开发日本英语学习者朗读和跟读表现自动评估系统

基本信息

批准号：
21320105
负责人：
YAMAUCHI Yutaka
金额：
$ 11.56万
依托单位：
Tokyo International University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2009
资助国家：
日本
起止时间：
2009 至 2012
项目状态：
已结题

项目摘要

Although shadowing is considered to be effective in improving EFL learners’ listening and speaking skills, it is both difficult and time consuming to assess shadowing performance. To solve this problem, an automatic assessing system has been newly developed using the latest speech information technology: GOP (Goodness of Pronunciation). The computer automatically compares the learner’s shadowed speech with an acous tic model based on the original text on a phoneme basis and computes numerical scores which indicate how close the learner’s shadowed speech is to the original. To confirm the validity of this automatic assessment, an experiment was conducted. Japanese EFL learners with proficiency levels ranging from 200s to 900s measured by TOEIC scores were requested to record their shadowing into the computer while listening to passages, which had been never presented before, read by a native speaker of English at a speed of approximately 140 words per minute. Then the scores obtained by the automatic system were compared to those rated manually by veteran EFL instructors. A high correlation (r=0.85) was observed between the two; thus this system is significantly valid and also saves much time and energy in assessing shadowing performance. Since the automatic scores of the learners were also highly correlated with their TOEIC scores (r=0.82), the shadowing performance can predict their overall proficiency scores. Based on the regression model, this system can predict and present TOEIC score levels. Therefore this automatic assessing system enables EFL learners to understand objectively how well they have conducted shadowing and also grasp their own proficiency levels.

尽管阴影被认为可以有效地提高EFL学习者的聆听和口语技巧，但评估阴影性能既困难又耗时。为了解决这个问题，使用最新的语音信息技术新开发了自动评估系统：GOP（发音的好处）。计算机会根据音素的原始文本自动比较学习者的阴影语音与声学抽搐模型，并计算数值分数，这些数字得分表明学习者的阴影语音与原始内容有多近。为了确认该自动评估的有效性，进行了一个实验。通过Toeic分数测量的熟练程度从200到900的日本EFL学习者被要求记录他们的阴影到计算机中，同时聆听段落（从未有过的段落，英语的母语人士都以每分钟约140个单词的速度阅读。然后将自动系统获得的分数与经验丰富的EFL讲师手动评级的分数进行比较。在两者之间观察到高相关性（r = 0.85）。因此，该系统是显着有效的，并且还节省了评估阴影性能的时间和精力。由于学习者的自动分数也与他们的toeic得分高度相关（r = 0.82），因此阴影性能可以预测其整体水平得分。基于回归模型，该系统可以预测和呈现toeic得分水平。因此，这种自动评估系统使EFL学习者能够客观地理解他们的阴影能力以及自己的能力水平。