Unified Multi-task Learning for Voice-Based Detection of Diverse Clinical Conditions

• URL: http://arxiv.org/abs/2508.20717v1
• Date: Thu, 28 Aug 2025 12:37:25 GMT
• Title: Unified Multi-task Learning for Voice-Based Detection of Diverse Clinical Conditions
• Authors: Ran Piao, Yuan Lu, Hareld Kemps, Tong Xia, Aaqib Saeed,
• Abstract summary: We present MARVEL, a privacy-conscious multitask learning framework that simultaneously detects nine distinct neurological, respiratory, and voice disorders.<n>Our framework consistently outperforms single-modal baselines by 5-19% and surpasses state-of-the-art self-supervised models on 7 of 9 tasks.
• Score: 14.745982411183766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Voice-based health assessment offers unprecedented opportunities for scalable, non-invasive disease screening, yet existing approaches typically focus on single conditions and fail to leverage the rich, multi-faceted information embedded in speech. We present MARVEL (Multi-task Acoustic Representations for Voice-based Health Analysis), a privacy-conscious multitask learning framework that simultaneously detects nine distinct neurological, respiratory, and voice disorders using only derived acoustic features, eliminating the need for raw audio transmission. Our dual-branch architecture employs specialized encoders with task-specific heads sharing a common acoustic backbone, enabling effective cross-condition knowledge transfer. Evaluated on the large-scale Bridge2AI-Voice v2.0 dataset, MARVEL achieves an overall AUROC of 0.78, with exceptional performance on neurological disorders (AUROC = 0.89), particularly for Alzheimer's disease/mild cognitive impairment (AUROC = 0.97). Our framework consistently outperforms single-modal baselines by 5-19% and surpasses state-of-the-art self-supervised models on 7 of 9 tasks, while correlation analysis reveals that the learned representations exhibit meaningful similarities with established acoustic features, indicating that the model's internal representations are consistent with clinically recognized acoustic patterns. By demonstrating that a single unified model can effectively screen for diverse conditions, this work establishes a foundation for deployable voice-based diagnostics in resource-constrained and remote healthcare settings.

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