← 研踪 PaperTrailarXiv:2611.13544 · v1
DASWeave: Weaving Evidence across Speaking Conditions for Multimodal Depression, Anxiety, and Stress Screening
首次提交 2026-11-10 12:07
版本更新 2026-11-10 12:07
雷达观测 2026-11-11 13:08
摘要
Multimodal screening for depression, anxiety, and stress in children and adolescents uses verbal and nonverbal behaviour. Most existing multimodal methods learn from a single recording or interview. Yet a participant's behaviour changes with the speaking condition, and these changes hold evidence that is invisible in a single recording. We propose DASWeave to bring this evidence into a neural network that encodes each response and then combines all responses of a participant. We train supervised trees to find which behavioural statistics matter for screening, both within each response and in how they change across conditions. We feed the tree outputs into the network both before and after this combination. We fine-tune a large language model to relate what a participant says to how they say it across conditions, and feed its representation into the network after the combination. The language model reads behaviour as text descriptions. We therefore derive additional training targets for the network from a multimodal large language model that takes in the audio-visual features directly. On the official hidden test set of the ACM Multimedia 2026 AdoDAS Grand Challenge (Track 1), DASWeave ranks second among 95 eligible teams. Its mean F1 is 27.1% higher than that of the official audio-visual baseline. It also has the highest mean AUROC among the top five teams. Ablation studies on the validation set show that the supervised trees and the fine-tuned language model contribute most to mean F1.
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v1 · 2026-11-10 12:07 · 最新观测版本
DASWeave: Weaving Evidence across Speaking Conditions for Multimodal Depression, Anxiety, and Stress Screening
Multimodal screening for depression, anxiety, and stress in children and adolescents uses verbal and nonverbal behaviour. Most existing multimodal methods learn from a single recording or interview. Yet a participant's behaviour changes with the speaking condition, and these changes hold evidence that is invisible in a single recording. We propose DASWeave to bring this evidence into a neural network that encodes each response and then combines all responses of a participant. We train supervised trees to find which behavioural statistics matter for screening, both within each response and in how they change across conditions. We feed the tree outputs into the network both before and after this combination. We fine-tune a large language model to relate what a participant says to how they say it across conditions, and feed its representation into the network after the combination. The language model reads behaviour as text descriptions. We therefore derive additional training targets for the network from a multimodal large language model that takes in the audio-visual features directly. On the official hidden test set of the ACM Multimedia 2026 AdoDAS Grand Challenge (Track 1), DASWeave ranks second among 95 eligible teams. Its mean F1 is 27.1% higher than that of the official audio-visual baseline. It also has the highest mean AUROC among the top five teams. Ablation studies on the validation set show that the supervised trees and the fine-tuned language model contribute most to mean F1.
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