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Dysarthria detection is crucial for clinical diagnosis and treatment. However, existing methods predominantly rely on supervised learning, which requires extensive annotated data, resulting in high costs and inconsistent data quality. To address this issue, this paper proposes a feature extraction method for dysarthria detection based on contrastive learning, which does not require annotated data. This method investigates how to extract features from patients and normal individuals using different pre-trained acoustic models. By maximizing the differences in their acoustic feature spaces, this method enhances detection accuracy. Finally, multiple classification methods are employed to detect dysarthria using the extracted features, achieving significant improvements across various evaluation metrics.

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Feature Extraction Method Based on Contrastive Learning for Dysarthria Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Yudong Yang,
  • Xinyi Wu,
  • Xiaokang Liu,
  • Juan Liu,
  • Jingdong Zhou,
  • Rennan Wang,
  • Xin Wang,
  • Rongfeng Su,
  • Nan Yan,
  • Lan Wang

摘要

Dysarthria detection is crucial for clinical diagnosis and treatment. However, existing methods predominantly rely on supervised learning, which requires extensive annotated data, resulting in high costs and inconsistent data quality. To address this issue, this paper proposes a feature extraction method for dysarthria detection based on contrastive learning, which does not require annotated data. This method investigates how to extract features from patients and normal individuals using different pre-trained acoustic models. By maximizing the differences in their acoustic feature spaces, this method enhances detection accuracy. Finally, multiple classification methods are employed to detect dysarthria using the extracted features, achieving significant improvements across various evaluation metrics.