The suitability of cloud-based speech recognition engines for language learning

  • Paul Daniels Kochi University of Technology
  • Koji Iwago University of Kochi
Keywords: CALL, Speech recognition, Google, Siri

Abstract

As online automatic speech recognition (ASR) engines become more accurate and more widely implemented with CALL software, it becomes important to evaluate the effectiveness and the accuracy of these recognition engines using authentic speech samples. This study investigates two of the most prominent cloud-based speech recognition engines- Apple’s Siri and Google Speech Recognition (GSR) to determine which engine would be more accurate at transcribing L2 learners’ speech. The average recognition accuracy of Siri and GSR is reported using language samples of Japanese learners speaking English. The study also presents a series of computerized speech assessment tasks that were developed by the researchers using a cloud-based speech recognition engine in conjunction with Moodle, a widely used course management system.

Author Biographies

Paul Daniels, Kochi University of Technology

CORE Studies

Professor

Koji Iwago, University of Kochi
Language instructor

References

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Copyright ©2015, American Society for Engineering Education, Washington DC. Retrieved from http://www.indiana.edu/~ciec/Proceedings_2015/ETD/ETD315_Ploger.pdf
Published
2017-12-23
Section
Forums