Evaluating Parkinson's Disease Detection in Anonymized Speech: A Performance and Acoustic Analysis
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917322593140736 |
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| author | Franzreb, Carlos Teixeira, Francisco Luks, Ben Möller, Sebastian Abad, Alberto |
| author_facet | Franzreb, Carlos Teixeira, Francisco Luks, Ben Möller, Sebastian Abad, Alberto |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07544 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evaluating Parkinson's Disease Detection in Anonymized Speech: A Performance and Acoustic Analysis Franzreb, Carlos Teixeira, Francisco Luks, Ben Möller, Sebastian Abad, Alberto Sound Audio and Speech Processing 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. |
| title | Evaluating Parkinson's Disease Detection in Anonymized Speech: A Performance and Acoustic Analysis |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2603.07544 |