用语音分析早期识别帕金森病，还能解释关键判断依据
Interpretable Early Detection of Parkinson's Disease through Speech Analysis
- 基于深度学习提取语音中影响诊断的关键特征
- 在831段语音上达到与顶尖方法相当的准确率
- 适合临床辅助诊断与神经机制研究者使用
帕金森病是一种进行性神经退行性疾病，早期即出现言语障碍。本研究提出一种深度学习方法，通过语音录音实现帕金森病的早期检测，并揭示推动预测结果的关键声学片段，提升模型可解释性。该方法尝试将预测特征与发音器官功能关联，为理解神经肌肉损伤机制提供依据。实验基于意大利帕金森语音数据库（Italian Parkinson's Voice and Speech Database），包含65名参与者共831段音频（健康人与患者）。所提方法在分类性能上优于或媲美现有先进方法，同时能有效识别影响判断的核心语音特征。
原文摘要 · Abstract (English)
Parkinson's disease is a progressive neurodegenerative disorder affecting motor and non-motor functions, with speech impairments among its earliest symptoms. Speech impairments offer a valuable diagnostic opportunity, with machine learning advances providing promising tools for timely detection. In this research, we propose a deep learning approach for early Parkinson's disease detection from speech recordings, which also highlights the vocal segments driving predictions to enhance interpretability. This approach seeks to associate predictive speech patterns with articulatory features, providing a basis for interpreting underlying neuromuscular impairments. We evaluated our approach using the Italian Parkinson's Voice and Speech Database, containing 831 audio recordings from 65 participants, including both healthy individuals and patients. Our approach showed competitive classification performance compared to state-of-the-art methods, while providing enhanced interpretability by identifying key speech features influencing predictions.
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