End-to-End Mispronunciation Detection and Diagnosis From Raw Waveforms

Bi Cheng Yan, Berlin Chen

研究成果: 書貢獻/報告類型會議論文篇章

摘要

Mispronunciation detection and diagnosis (MDD) is designed to identify pronunciation errors and provide instructive feedback to guide non-native language learners, which is a core component in computer-assisted pronunciation training (CAPT) systems. However, MDD often suffers from the data-sparsity problem due to that collecting non-native data and the associated annotations is time-consuming and labor-intensive. To address this issue, we explore a fully end-to-end (E2E) neural model for MDD, which processes learners' speech directly based on raw waveforms. Compared to conventional hand-crafted acoustic features, raw waveforms retain more acoustic phenomena and potentially can help neural networks discover better and more customized representations. To this end, our MDD model adopts a co-called SincNet module to take input a raw waveform and covert it to a suitable vector representation sequence. SincNet employs the cardinal sine (sinc) function to implement learnable bandpass filters, drawing inspiration from the convolutional neural network (CNN). By comparison to CNN, SincNet has fewer parameters and is more amenable to human interpretation. Extensive experiments are conducted on the L2-ARCTIC dataset, which is a publicly-available non-native English speech corpus compiled for research on CAPT. We find that the sinc filters of SincNet can be adapted quickly for non-native language learners of different nationalities. Furthermore, our model can achieve comparable mispronunciation detection performance in relation to state-of-the-art E2E MDD models that take input the standard handcrafted acoustic features. Besides that, our model also provides considerable improvements on phone error rate (PER) and diagnosis accuracy.

原文英語
主出版物標題29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
發行者European Signal Processing Conference, EUSIPCO
頁面61-65
頁數5
ISBN(電子)9789082797060
DOIs
出版狀態已發佈 - 2021
事件29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, 愛爾蘭
持續時間: 2021 8月 232021 8月 27

出版系列

名字European Signal Processing Conference
2021-August
ISSN(列印)2219-5491

會議

會議29th European Signal Processing Conference, EUSIPCO 2021
國家/地區愛爾蘭
城市Dublin
期間2021/08/232021/08/27

ASJC Scopus subject areas

  • 訊號處理
  • 電氣與電子工程

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