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    <abstract>Vocal behavior provides important signals for speech-based depression detection, but its interpretation often depends on what is being said and how it functions in context. However, existing methods typically treat acoustic cues as context-independent markers, making it difficult to distinguish vocal form from its context-dependent communicative function. We propose ParaCalib, a framework that semantically calibrates paralinguistic behavior by interpreting vocal patterns relative to utterance-level semantic context and inferred communicative function and representing them in a comparable state space. Concretely, ParaCalib uses an Audio-Language Model (ALM) to generate contextualized vocal descriptions and an LLM-based Paralinguistic State Extractor (PSE) to map these descriptions into a structured Semantically Calibrated Paralinguistic (SC-Para) representation. ParaCalib achieves the highest mean Macro-F1 among the evaluated methods, reaching 71.9% on DAIC-WOZ and 90.5% on MODMA. Our controlled analysis provides direct evidence of semantic calibration at the PSE stage: under a fixed caption-level acoustic description, varying the accompanying semantic context changes the inferred depression-related paralinguistic evidence. Our exploratory analysis further identifies recurring configurations of paralinguistic states associated with depression labels rather than a single uniformly dominant state.</abstract>
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%0 Conference Proceedings
%T ParaCalib: Semantically Calibrated Paralinguistic Modeling for Depression Detection
%A Li, Yuxin
%A Li, Yifei
%A Chao, Yi-Wen
%A Zhang, Xiangyu
%A Chng, Eng Siong
%A Guan, Cuntai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-paracalib
%X Vocal behavior provides important signals for speech-based depression detection, but its interpretation often depends on what is being said and how it functions in context. However, existing methods typically treat acoustic cues as context-independent markers, making it difficult to distinguish vocal form from its context-dependent communicative function. We propose ParaCalib, a framework that semantically calibrates paralinguistic behavior by interpreting vocal patterns relative to utterance-level semantic context and inferred communicative function and representing them in a comparable state space. Concretely, ParaCalib uses an Audio-Language Model (ALM) to generate contextualized vocal descriptions and an LLM-based Paralinguistic State Extractor (PSE) to map these descriptions into a structured Semantically Calibrated Paralinguistic (SC-Para) representation. ParaCalib achieves the highest mean Macro-F1 among the evaluated methods, reaching 71.9% on DAIC-WOZ and 90.5% on MODMA. Our controlled analysis provides direct evidence of semantic calibration at the PSE stage: under a fixed caption-level acoustic description, varying the accompanying semantic context changes the inferred depression-related paralinguistic evidence. Our exploratory analysis further identifies recurring configurations of paralinguistic states associated with depression labels rather than a single uniformly dominant state.
%U https://aclanthology.org/2026.findings-emnlp.1284/
%P 27197-27214

Markdown (Informal)

[ParaCalib: Semantically Calibrated Paralinguistic Modeling for Depression Detection](https://aclanthology.org/2026.findings-emnlp.1284/) (Li et al., Findings 2026)

• ParaCalib: Semantically Calibrated Paralinguistic Modeling for Depression Detection (Li et al., Findings 2026)

ACL

• Yuxin Li, Yifei Li, Yi-Wen Chao, Xiangyu Zhang, Eng Siong Chng, and Cuntai Guan. 2026. ParaCalib: Semantically Calibrated Paralinguistic Modeling for Depression Detection. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 27197–27214, Budapest, Hungary. Association for Computational Linguistics.