发表机构
Nanyang Technological University; University of New South Wales（南洋理工大学; 新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ParaCalib通过音频语言模型和基于大语言模型的提取器，将语音模式与语义语境校准，实现副语言状态的结构化表示，在抑郁检测中取得最高平均宏F1（DAIC-WOZ 71.9%，MODMA 90.5%）。
AI 中文摘要
语音行为为基于语音的抑郁检测提供了重要信号，但其解读往往取决于说话内容及其在语境中的功能。然而，现有方法通常将声学线索视为与语境无关的标记，这使得区分语音形式与其依赖语境的交际功能变得困难。我们提出ParaCalib框架，该框架通过相对于话语级语义语境和推断的交际功能来解读语音模式，并将其表示在可比较的状态空间中，从而对副语言行为进行语义校准。具体而言，ParaCalib使用音频语言模型（ALM）生成语境化的语音描述，并利用基于大语言模型的副语言状态提取器（PSE）将这些描述映射为结构化的语义校准副语言（SC-Para）表示。ParaCalib在评估方法中取得了最高的平均宏F1分数，在DAIC-WOZ上达到71.9%，在MODMA上达到90.5%。我们的受控分析提供了PSE阶段语义校准的直接证据：在固定的字幕级声学描述下，改变伴随的语义语境会改变推断的与抑郁相关的副语言证据。我们的探索性分析进一步识别出与抑郁标签相关的副语言状态的重复配置，而非单一的普遍主导状态。
英文摘要
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.
