Semi-Supervised Diseased Detection from Speech Dialogues with Multi-Level Data Modeling

• URL: http://arxiv.org/abs/2601.04744v1
• Date: Thu, 08 Jan 2026 09:10:16 GMT
• Title: Semi-Supervised Diseased Detection from Speech Dialogues with Multi-Level Data Modeling
• Authors: Xingyuan Li, Mengyue Wu,
• Abstract summary: We propose a novel framework for learning to detect medical conditions from speech acoustics.<n>Our end-to-end approach dynamically aggregates multi-granularity features and generates high-quality pseudo-labels.<n>This work provides a principled approach to learning from weak, far-end supervision in medical speech analysis.
• Score: 27.224093715611534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting medical conditions from speech acoustics is fundamentally a weakly-supervised learning problem: a single, often noisy, session-level label must be linked to nuanced patterns within a long, complex audio recording. This task is further hampered by severe data scarcity and the subjective nature of clinical annotations. While semi-supervised learning (SSL) offers a viable path to leverage unlabeled data, existing audio methods often fail to address the core challenge that pathological traits are not uniformly expressed in a patient's speech. We propose a novel, audio-only SSL framework that explicitly models this hierarchy by jointly learning from frame-level, segment-level, and session-level representations within unsegmented clinical dialogues. Our end-to-end approach dynamically aggregates these multi-granularity features and generates high-quality pseudo-labels to efficiently utilize unlabeled data. Extensive experiments show the framework is model-agnostic, robust across languages and conditions, and highly data-efficient-achieving, for instance, 90\% of fully-supervised performance using only 11 labeled samples. This work provides a principled approach to learning from weak, far-end supervision in medical speech analysis.

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