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[2602.18535] Fairness-Aware Partial-label Domain Adaptation for Voice Classification of Parkinson's and ALS
Summary
This paper presents a novel framework for voice classification of Parkinson's and ALS using fairness-aware partial-label domain adaptation, addressing challenges in cross-device and cohort variability.
Why It Matters
The research is significant as it tackles the pressing issue of model performance variability in real-world applications, particularly in healthcare settings. By ensuring fairness and reducing gender bias, it enhances the reliability of voice-based diagnostics for neurodegenerative diseases, potentially improving patient outcomes.
Key Takeaways
- Introduces a hybrid framework for voice classification in Parkinson's and ALS.
- Addresses challenges of domain shift and partial-label mismatch.
- Incorporates adversarial methods to promote gender-invariant representations.
- Demonstrates superior performance over existing methods in external generalization.
- Establishes a benchmark for cross-cohort voice classification.
Computer Science > Sound arXiv:2602.18535 (cs) [Submitted on 20 Feb 2026] Title:Fairness-Aware Partial-label Domain Adaptation for Voice Classification of Parkinson's and ALS Authors:Arianna Francesconi, Zhixiang Dai, Arthur Stefano Moscheni, Himesh Morgan Perera Kanattage, Donato Cappetta, Fabio Rebecchi, Paolo Soda, Valerio Guarrasi, Rosa Sicilia, Mary-Anne Hartley View a PDF of the paper titled Fairness-Aware Partial-label Domain Adaptation for Voice Classification of Parkinson's and ALS, by Arianna Francesconi and 9 other authors View PDF HTML (experimental) Abstract:Voice-based digital biomarkers can enable scalable, non-invasive screening and monitoring of Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS). However, models trained on one cohort or device often fail on new acquisition settings due to cross-device and cross-cohort domain shift. This challenge is amplified in real-world scenarios with partial-label mismatch, where datasets may contain different disease labels and only partially overlap in class space. In addition, voice-based models may exploit demographic cues, raising concerns about gender-related unfairness, particularly when deployed across heterogeneous cohorts. To tackle these challenges, we propose a hybrid framework for unified three-class (healthy/PD/ALS) cross-domain voice classification from partially overlapping cohorts. The method combines style-based domain generalization with conditional adversarial alignment tailored to par...
