2012 Fiscal Year Final Research Report
Development of an Automatic Evaluation System of Reading-Aloud and Shadowing Performances by Japanese EFL Learners Using the Latest Speech Technology
| Project/Area Number |
21320105
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Foreign language education
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| Research Institution | Tokyo International University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
MINEMATSU Nobuaki 東京大学, 大学院・工学系研究科, 教授 (90273333)
KAWAMURA Akemi 東京国際大学, 言語コミュニケーション学部, 准教授 (30326996)
HUSKY Kay 東京国際大学, 商学部, 准教授 (50237955)
SOMEYA Yasumasa 関西大学, 外国語学部, 教授 (40348454)
NISHIKAWA Megumi 東海大学, 外国語教育センター, 准教授 (10453705)
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| Project Period (FY) |
2009 – 2012
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| Keywords | 教育評価 / 測定 |
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| Research Abstract |
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.
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