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Related Experiment Video

Updated: Oct 24, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Non-native acoustic modeling for mispronunciation verification based on language adversarial representation learning.

Longfei Yang1, Kaiqi Fu2, Jinsong Zhang2

  • 1Department of Information and Communication Engineering, Tokyo Institute of Technology, Tokyo, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|August 13, 2021
PubMed
Summary

This study introduces a novel pre-trained approach for non-native mispronunciation verification, leveraging native language speech data to overcome data sparsity in computer-aided pronunciation training (CAPT). The method effectively improves pronunciation error detection and feedback for language learners.

Keywords:
Computer aided pronunciation trainingLanguage adversarial trainingMispronunciation verificationNon-native acoustic modelingUnsupervised learning

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Area of Science:

  • Speech Processing
  • Computational Linguistics
  • Language Acquisition

Background:

  • Non-native mispronunciation verification is crucial for computer-aided pronunciation training (CAPT) systems.
  • Existing methods face data sparsity issues due to the difficulty of collecting and annotating non-native speech data.
  • This limits the effectiveness of current pronunciation feedback for language learners.

Purpose of the Study:

  • To propose a pre-trained approach for non-native mispronunciation verification that utilizes speech data from both the learner's native and target languages.
  • To address the data sparsity problem inherent in traditional CAPT systems.
  • To enhance the accuracy and utility of pronunciation error detection and feedback.

Main Methods:

  • An unsupervised model was developed to extract knowledge from large-scale unlabeled target language speech data.
  • Language adversarial training was employed using the learner's native language to align feature distributions.
  • A sinc filter was incorporated to capture formant-like features, aiding in articulation analysis.

Main Results:

  • The pre-trained model effectively utilized knowledge from native language speech for non-native phone recognition and mispronunciation verification.
  • Language adversarial representation learning significantly improved performance in these tasks.
  • The inclusion of formant-like features via sinc filters further enhanced mispronunciation verification accuracy.

Conclusions:

  • The proposed unsupervised pre-training approach effectively leverages native language speech data to improve non-native mispronunciation verification.
  • Language adversarial training and formant-like feature extraction are key components for enhancing CAPT system performance.
  • This method offers a promising solution for more accurate and instructive pronunciation feedback in language learning.