Weakly-Supervised Word-Level Pronunciation Error Detection in Non-Native English Speech - Publication - Bridge of Knowledge

Search

Weakly-Supervised Word-Level Pronunciation Error Detection in Non-Native English Speech

Abstract

We propose a weakly-supervised model for word-level mispronunciation detection in non-native (L2) English speech. To train this model, phonetically transcribed L2 speech is not required and we only need to mark mispronounced words. The lack of phonetic transcriptions for L2 speech means that the model has to learn only from a weak signal of word-level mispronunciations. Because of that and due to the limited amount of mispronounced L2 speech, the model is more likely to overfit. To limit this risk, we train it in a multi-task setup. In the first task, we estimate the probabilities of word-level mispronunciation. For the second task, we use a phoneme recognizer trained on phonetically transcribed L1 speech that is easily accessible and can be automatically annotated. Compared to state-of-the-art approaches, we improve the accuracy of detecting word-level pronunciation errors in AUC metric by 30% on the GUT Isle Corpus of L2 Polish speakers, and by 21.5% on the Isle Corpus of L2 German and Italian speakers

Citations

  • 1 1

    CrossRef

  • 0

    Web of Science

  • 8

    Scopus

Authors (5)

Cite as

Full text

download paper
downloaded 50 times
Publication version
Accepted or Published Version
DOI:
Digital Object Identifier (open in new tab) 10.21437/Interspeech.2021-38
License
Copyright (2021 ISCA)

Keywords

Details

Category:
Conference activity
Type:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Language:
English
Publication year:
2021
Bibliographic description:
Korzekwa D., Lorenzo-Trueba J., Drugman T., Calamaro S., Kostek B.: Weakly-Supervised Word-Level Pronunciation Error Detection in Non-Native English Speech// / : , 2021,
DOI:
Digital Object Identifier (open in new tab) 10.21437/interspeech.2021-38
Sources of funding:
  • Statutory activity/subsidy
Verified by:
Gdańsk University of Technology

seen 88 times

Recommended for you

Meta Tags