An end-to-end mispronunciation detection system for L2 English speech leveraging novel anti-phone modeling

Bi Cheng Yan, Meng Che Wu, Hsiao Tsung Hung, Berlin Chen

研究成果: 雜誌貢獻會議論文同行評審

3 引文 斯高帕斯(Scopus)

摘要

Mispronunciation detection and diagnosis (MDD) is a core component of computer-assisted pronunciation training (CAPT). Most of the existing MDD approaches focus on dealing with categorical errors (viz. one canonical phone is substituted by another one, aside from those mispronunciations caused by deletions or insertions). However, accurate detection and diagnosis of non-categorial or distortion errors (viz. approximating L2 phones with L1 (first-language) phones, or erroneous pronunciations in between) still seems out of reach. In view of this, we propose to conduct MDD with a novel end-to-end automatic speech recognition (E2E-based ASR) approach. In particular, we expand the original L2 phone set with their corresponding anti-phone set, making the E2E-based MDD approach have a better capability to take in both categorical and non-categorial mispronunciations, aiming to provide better mispronunciation detection and diagnosis feedback. Furthermore, a novel transfer-learning paradigm is devised to obtain the initial model estimate of the E2E-based MDD system without resource to any phonological rules. Extensive sets of experimental results on the L2-ARCTIC dataset show that our best system can outperform the existing E2E baseline system and pronunciation scoring based method (GOP) in terms of the F1-score, by 11.05% and 27.71%, respectively.

原文英語
頁(從 - 到)3032-3036
頁數5
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2020-October
DOIs
出版狀態已發佈 - 2020
事件21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, 中国
持續時間: 2020 十月 252020 十月 29

ASJC Scopus subject areas

  • 語言與語言學
  • 人機介面
  • 訊號處理
  • 軟體
  • 建模與模擬

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