Tag
该研究利用跨三个自发双人互动数据集的多模态特征（发音、面部、声学韵律和话轮转换），预测自闭症与非自闭症互动中的感知对话成功度，表明对话成功本质上是多维的，需要多模态分析框架来提升预测效果。
FriendBench is a new benchmark for evaluating whether humans and multimodal LLMs can infer if two people are familiar or strangers from a 20-second video clip of an ice-breaker conversation. Results show the best models match human accuracy but differ in bias, and only humans benefit from richer visual behavior.
This paper evaluates the predictive accuracy, cross-task generalizability, and test-retest reliability of multimodal features for measuring conversational states like cognitive load and power in dyadic remote collaborative tasks. Findings show that linguistic features predict well but generalize poorly, acoustic reliability degrades when controlling for speaker identity, and interaction features provide the most reliable signal.
This paper introduces DEPOOL, a controlled benchmark evaluating six temporal aggregation architectures across six frozen speech backbones for depression detection in dyadic interactions, finding that many configurations collapse into single-class predictions and that robustness should be a key criterion.