Employing Automatic Speech Recognition for Quantitative Oral Corrective Feedback in Japanese Second or Foreign Language Education
Employing Automatic Speech Recognition for Quantitative Oral Corrective Feedback in Japanese Second or Foreign Language Education
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在日语第二语言或外语教育中采用自动语音识别进行定量口头纠正反馈
DOI：
10.1145/3369255.3369285
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Kotaro Kataoka
中科院分区：
文献类型：
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作者：
Yuka Kataoka;A. Thamrin;J. Murai;Kotaro Kataoka
在第二语言或外语（SFL）教育中，许多应用语言学研究都解决了一个常见问题，即教师如何在课堂上提供有效的反馈来纠正学习者的错误话语。对于教师来说，口头纠正反馈（OCF）通常是一项耗时且费力的工作。在SFL教育中使用ASR（自动语音识别）来提高学习效果和效率已引起教师和学习者的关注。我们使用 Google Cloud Speech-to-Text 设计并集成了定量 OCF，作为日语 SFL 课程使用 LMS（学习管理系统）的口语评估的一部分。学习者的水平是初级水平，没有任何日语基础知识。使用非母语人士的总共 214 个音频数据集进行的初步实验表明，37.4% 的数据集被正确识别为日语句子。然而，由于数据集的其余部分包含错误的话语、语调特征或噪声，ASR 成功检测到基于单词的错误，准确率较高 (82.4%)，但精度较低 (28.1%)。采用 ASR 的口语评估作为教师的补充系统非常有前景，可以部分自动化地对来自学习者的音频数据进行证据和优先顺序的评估，并显着减少教师的评分工作量和花在学生演讲中最有问题的部分上的时间。虽然我们的实施仍然需要教师仔细检查，但这样的开销很小并且可以承受。
In Second or Foreign Language (SFL) education, a number of studies in applied linguistics have addressed a common issue of how teachers can provide effective feedback to correct learner's erroneous utterances during a classroom hour. Oral Corrective Feedback (OCF) is generally time-consuming and labor-intensive work for teachers. The use of ASR (Automatic Speech Recognition) in SFL education has drawn attention from both teachers and learners to increase the learning effect and efficiency. We designed and integrated Quantitative OCF using Google Cloud Speech-to-Text as a part of the oral assessment using an LMS (Learning Management System) for Japanese SFL courses. The level of learners is a starter's level without any prerequisite knowledge of Japanese language. Preliminary experiments using a total of 214 audio datasets by non-native speakers exhibited that 37.4% of the datasets were recognized properly as Japanese sentences. However, as the remainder of the datasets contains erroneous utterances, characteristics of intonation, or noise, ASR successfully detected word-based errors with high accuracy (82.4%) but low precision (28.1%). Oral assessment employing ASR is highly promising as a complementary system for teachers on partially automating the assessment of audio data from learners with evidence and priority orders as well as significantly reducing teachers' scoring workload and time spent on the most problematic part of the students' speech. While our implementation still requires teachers to double-check, such overhead is small and affordable.
使用Moodle API操作多服务器的实例以及原始API介绍
DOI：
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发表时间：
2023
期刊：
影响因子：
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作者：
大西淑雅;山口真之介
通讯作者：
山口真之介