AI 中文总结
研究针对心理健康评估中通用融合模型忽略问卷标签心理测量结构的问题，提出DynaBridge框架，通过编码多模态线索并结合语义摘要进行评估，在官方验证分割上优于基线和其他方法，展现了多模态融合与心理测量结构结合的价值。
AI 中文摘要
多模态行为分析为评估抑郁、焦虑和压力提供了一种可扩展的方法，但通用融合模型往往忽略问卷标签的心理测量结构。在DASS-21中，风险标签通过固定的项目到子量表映射从有序症状项目中得出。我们提出了DynaBridge，一种用于DASS结构心理健康评估的动态摘要引导跨任务多模态框架。DynaBridge对多个会话中的声学、视觉和文本线索进行编码，并用冻结的语言模型生成的DASS感知摘要作为参与者级别的语义证据对其进行增强。它预测序数项目分布，从项目级软分数中重建抑郁、焦虑和压力风险证据，并将此证据与直接的多模态风险预测相融合。一种置信度感知细化策略进一步保守地纳入高置信度语义线索。在官方AdoDAS验证分割上，DynaBridge优于官方基线和代表性多模态方法，在D/A/S风险预测中实现了0.5012的平均F1，在DASS-21项目预测中实现了0.3216的平均QWK。这些结果显示了桥接多模态线索、语义摘要和DASS-21心理测量结构的价值。
英文摘要
Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questionnaire labels. In DASS-21, risk labels are derived from ordered symptom items through fixed item-to-subscale mappings. We propose \textbf{DynaBridge}, a dynamic summary-guided cross-task multimodal framework for DASS-structured mental health assessment. DynaBridge encodes acoustic, visual, and textual cues across multiple sessions and augments them with frozen-LLM-generated DASS-aware summaries as participant-level semantic evidence. It predicts ordinal item distributions, reconstructs depression, anxiety, and stress risk evidence from item-level soft scores, and fuses this evidence with direct multimodal risk predictions. A confidence-aware refinement strategy further incorporates high-confidence semantic cues conservatively. On the official AdoDAS validation split, DynaBridge outperforms the official baseline and representative multimodal methods, achieving 0.5012 mean F1 for D/A/S risk prediction and 0.3216 mean QWK for DASS-21 item prediction. These results show the value of bridging multimodal cues, semantic summaries, and DASS-21 psychometric structure.
