Abstract
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.
Abstract (translated)
从语音中自动检测帕金森病（PD）是一种有前景的非侵入性诊断工具，但这种方法会引发重大的隐私问题。说话人匿名化技术可以减轻这些风险，但它可能会抑制用于PD检测所需的关键病理信息。我们使用两个西班牙语数据集评估了两种匿名化方法（STT-TTS和kNN-VC）在保护隐私与PD检测准确性之间的权衡。 STT-TTS提供了更好的隐私保护，但这种方法会严重降低PD的检测能力，因为它消除了语音中的韵律特征。另一方面，kNN-VC能够保持音长、基频轮廓等宏观韵律特性，在两个数据集中实现的F1得分仅比原始基准低3%-7%，这表明在使用适当的匿名化技术时，可以实现既保护隐私又具有较高检测准确性的PD诊断。 最后，声学失真分析揭示了kNN-VC的一些特定弱点，并为设计能够在保持PD相关信息的同时更好地保护隐私的技术提供了见解。
URL
https://arxiv.org/abs/2603.07544
