Maximum F1-Score Training for End-to-End Mispronunciation Detection and Diagnosis of L2 English Speech

Bi Cheng Yan, Hsin Wei Wang, Shao Wei Fan Jiang, Fu An Chao, Berlin Chen

研究成果: 書貢獻/報告類型會議論文篇章

摘要

End-to-end (E2E) neural models are increasingly attracting attention as a promising modeling approach for mispronunciation detection and diagnosis (MDD). Typically, these models are trained by optimizing a cross-entropy criterion, which corresponds to improving the log-likelihood of the training data. However, there is a discrepancy between the objectives of model training and the MDD evaluation, since the performance of an MDD model is commonly evaluated in terms of F1-score instead of phone or word error rate (PER/WER). In view of this, we in this paper explore the use of a discriminative objective function for training E2E MDD models, which aims to maximize the expected F1-score directly. A series of experiments conducted on the L2-ARCTIC dataset show that our proposed method can yield considerable performance improvements in relation to some state-of-the-art E2E MDD approaches and the celebrated GOP method.

原文英語
主出版物標題ICME 2022 - IEEE International Conference on Multimedia and Expo 2022, Proceedings
發行者IEEE Computer Society
ISBN(電子)9781665485630
DOIs
出版狀態已發佈 - 2022
事件2022 IEEE International Conference on Multimedia and Expo, ICME 2022 - Taipei, 臺灣
持續時間: 2022 7月 182022 7月 22

出版系列

名字Proceedings - IEEE International Conference on Multimedia and Expo
2022-July
ISSN(列印)1945-7871
ISSN(電子)1945-788X

會議

會議2022 IEEE International Conference on Multimedia and Expo, ICME 2022
國家/地區臺灣
城市Taipei
期間2022/07/182022/07/22

ASJC Scopus subject areas

  • 電腦網路與通信
  • 電腦科學應用

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