Employing automatic speech recognition for quantitative oral corrective feedback in Japanese second or foreign language education

Yuka Kataoka, Achmad Husni Thamrin, Jun Murai, Kotaro Kataoka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2019 11th International Conference on Education Technology and Computers, ICETC 2019
PublisherAssociation for Computing Machinery
Pages52-58
Number of pages7
ISBN (Electronic)9781450372541
DOIs
Publication statusPublished - 2019 Oct 28
Event11th International Conference on Education Technology and Computers, ICETC 2019 - Amsterdam, Netherlands
Duration: 2019 Oct 282019 Oct 31

Publication series

NameACM International Conference Proceeding Series

Conference

Conference11th International Conference on Education Technology and Computers, ICETC 2019
CountryNetherlands
CityAmsterdam
Period19/10/2819/10/31

Keywords

  • Automatic Speech Recognition
  • Japanese Second or Foreign Language education
  • Quantitative Oral Corrective Feedback

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Kataoka, Y., Thamrin, A. H., Murai, J., & Kataoka, K. (2019). Employing automatic speech recognition for quantitative oral corrective feedback in Japanese second or foreign language education. In Proceedings of the 2019 11th International Conference on Education Technology and Computers, ICETC 2019 (pp. 52-58). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3369255.3369285