An English pronunciation learning system for Japanese students based on diagnosis of critical pronunciation errors
An English pronunciation learning system for Japanese students based on diagnosis of critical pronunciation errors
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DOI：
10.1017/s0958344004001314
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发表时间：
2004-05
期刊：
影响因子：
4.5
通讯作者：
Yasushi Tsubota;M. Dantsuji;Tatsuya Kawahara
中科院分区：
文献类型：
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作者：
Yasushi Tsubota;M. Dantsuji;Tatsuya Kawahara
我们开发了一个英语发音学习系统，该系统从提高日语学习者对本族语者的可懂度的角度来评估日语学习者的语音可懂度，并对他们的错误进行排名。错误诊断在自学中尤其重要，因为学生往往花时间在发音方面，而这些方面并不明显影响可理解性。作为初步实验，一位语言学专家对7名日本学生的语音进行了从1（几乎听不懂）到5（完全听得懂）的评分。我们还计算了他们每种技能的错误率。我们发现，每个可懂度水平的特点是其分布的错误率。因此，我们根据其错误率对每个可懂度级别进行建模。通过比较学生的错误率分布与每个可懂度水平的相应模型的错误率分布来计算错误优先级。由于非母语语音在声学上比母语者的语音更广泛，我们开发了一个声学模型，使用从日本学生获得的语音数据进行自动错误检测。至于超分段错误检测，我们分类的错误，经常由日本学生和开发一个单独的声学模型，这种类型的错误检测。使用该系统的语音学习包括两个阶段。在第一阶段，学生通过视频剪辑体验虚拟对话。他们会收到一份基于对话中检测到的发音错误的错误档案。使用的配置文件，学生能够抓住他们的发音错误，实际上降低他们的可理解性的特征趋势。在第二阶段，学生练习使用单词和短语纠正他们的个人错误。然后，他们会收到关于在这一轮练习中发现的错误的信息和纠正错误的指示。我们已经开始在京都大学的CALL课堂上使用这个系统。我们通过使用调查问卷和分析记录在服务器上的语音数据来评估系统性能，并将在本文中介绍我们的研究结果。
We have developed an English pronunciation learning system which estimates the intelligibility of Japanese learners' speech and ranks their errors from the viewpoint of improving their intelligibility to native speakers. Error diagnosis is particularly important in self-study since students tend to spend time on aspects of pronunciation that do not noticeably affect intelligibility. As a preliminary experiment, the speech of seven Japanese students was scored from 1 (hardly intelligible) to 5 (perfectly intelligible) by a linguistic expert. We also computed their error rates for each skill. We found that each intelligibility level is characterized by its distribution of error rates. Thus, we modeled each intelligibility level in accordance with its error rate. Error priority was calculated by comparing students' error rate distributions with that of the corresponding model for each intelligibility level. As non-native speech is acoustically broader than the speech of native speakers, we developed an acoustic model to perform automatic error detection using speech data obtained from Japanese students. As for supra-segmental error detection, we categorized errors frequently made by Japanese students and developed a separate acoustic model for that type of error detection. Pronunciation learning using this system involves two phases. In the first phase, students experience virtual conversation through video clips. They receive an error profile based on pronunciation errors detected during the conversation. Using the profile, students are able to grasp characteristic tendencies in their pronunciation errors which in effect lower their intelligibility. In the second phase, students practise correcting their individual errors using words and short phrases. They then receive information regarding the errors detected during this round of practice and instructions for correcting the errors. We have begun using this system in a CALL class at Kyoto University. We have evaluated system performance through the use of questionnaires and analysis of speech data logged in the server, and will present our findings in this paper.