Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment
arXiv:2609.00055v1 Announce Type: new Abstract: Self-supervised respiratory encoders lack semantic grounding in clinical domain needed for zero-shot inference, limiting their utility without task-specific labeled data. We propose a framework that aligns these encoders with medical terminology in a shared latent space turning them into a zero-shot-capable foundation model. To address paired data scarcity, we use a medical LLM to synthesize structured reports from metadata, creating dense semantic anchors for contrastive learning. Our training combines a sigmoid-based contrastive loss with encoder's native SSL objective and similarity-aware negative sampling to sharpen pathological boundaries. Across 9 tasks on 6 datasets, our method achieves a 61.3% mean zero-shot AUC, surpassing CLAP (51.4%) and Qwen2-Audio (54.9%) while reaching the highest linear probing AUC (71.6%) with only 43% of data used by full-scale baselines, showing that structured semantic alignment outperforms large-scale, general-purpose models in clinical diagnostics.
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[Submitted on 30 Aug 2026]
Title:Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment
View a PDF of the paper titled Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment, by Mustafa Talha \.Ilerisoy and 4 other authors
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Abstract:Self-supervised respiratory encoders lack semantic grounding in clinical domain needed for zero-shot inference, limiting their utility without task-specific labeled data. We propose a framework that aligns these encoders with medical terminology in a shared latent space turning them into a zero-shot-capable foundation model. To address paired data scarcity, we use a medical LLM to synthesize structured reports from metadata, creating dense semantic anchors for contrastive learning. Our training combines a sigmoid-based contrastive loss with encoder's native SSL objective and similarity-aware negative sampling to sharpen pathological boundaries. Across 9 tasks on 6 datasets, our method achieves a 61.3% mean zero-shot AUC, surpassing CLAP (51.4%) and Qwen2-Audio (54.9%) while reaching the highest linear probing AUC (71.6%) with only 43% of data used by full-scale baselines, showing that structured semantic alignment outperforms large-scale, general-purpose models in clinical diagnostics.
Comments: Accepted to INTERSPEECH 2026
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
Cite as: arXiv:2609.00055 [cs.CL]
(or arXiv:2609.00055v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.00055
arXiv-issued DOI via DataCite
Submission history
From: Mustafa Talha İlerisoy [view email] [v1] Sun, 30 Aug 2026 11:49:54 UTC (136 KB)
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