用语音匿名化技术实现帕金森病检测，兼顾隐私与准确率
Evaluating Parkinson's Disease Detection in Anonymized Speech: A Performance and Acoustic Analysis
- 采用STT-TTS和kNN-VC两种语音匿名方法
- kNN-VC使检测准确率下降仅3-7%
- 适合关注医疗隐私与疾病检测平衡的研究者
通过两个西班牙语数据集评估了两种语音匿名化方法（STT-TTS和kNN-VC）在帕金森病（PD）检测中的性能与隐私权衡。STT-TTS虽提供更好隐私保护，但严重削弱了语调信息，导致检测性能大幅下降；而kNN-VC保留了如持续时间和基频轮廓等宏观语调特征，其F1分数仅比原始基准低3-7%，证明在合适匿名化策略下，隐私保护的PD检测是可行的。此外，声学失真分析揭示了kNN-VC的具体缺陷，为未来设计更优的匿名化方法提供了依据。
原文摘要 · Abstract (English)
Automatic detection of Parkinson's disease (PD) from speech is a promising non-invasive diagnostic tool, but it raises significant privacy concerns. Speaker anonymization mitigates these risks, but it may suppress the pathological information necessary for PD detection. We assess the trade-off between privacy and PD detection for two anonymizers (STT-TTS and kNN-VC) using two Spanish datasets. STT-TTS provides better privacy but severely degrades PD detection by eradicating prosodic information. kNN-VC preserves macro-prosodic features such as duration and F0 contours, achieving F1 scores only 3-7\% lower than original baselines, demonstrating that privacy-preserving PD detection is viable when using appropriate anonymization. Finally, an acoustic distortion analysis characterizes specific weaknesses in kNN-VC, offering insights for designing anonymizers that better preserve PD information.
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