Using Automatic Speech Recognition to Facilitate English Pronunciation Assessment and Learning in an EFL Context: Pronunciation Error Diagnosis and Pedagogical Implications

Wenqi Xiao and Moonyoung Park
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Wenqi Xiao: Chinese University of Hong Kong, Hong Kong
Moonyoung Park: Chinese University of Hong Kong, Hong Kong

International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT), 2021, vol. 11, issue 3, 74-91

Abstract: With the advancement of automatic speech recognition (ASR) technology, ASR-based pronunciation assessment can diagnose learners' pronunciation problems. Meanwhile, ASR-based pronunciation training allows more opportunities for pronunciation practice. This study aims to investigate the effectiveness of ASR technology in diagnosing English pronunciation errors and to explore teachers' and learners' attitudes towards using ASR technology as a pronunciation assessment tool and as a learning tool. Five Chinese EFL learners participated in read-aloud tests, including a human-assessed test and an ASR-assessed test. Pronunciation error types diagnosed by the two tests were compared to determine the extent of overlapping areas. The findings demonstrate that there were overlaps between human rating and machine rating at the segmental level. Moreover, it was found that learners' varied pronunciation learning needs were met by using the ASR technology. Implications of the study will provide insights relevant to using ASR technology to facilitate English pronunciation assessment and learning.

Date: 2021
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