通过对话动态预测自然协作中认知负荷，效果优于传统语音分析。
Predicting Cognitive Load from Speech and Interaction Dynamics in Dyadic Conversations
- 用双头门控循环单元建模语音与互动特征
- 重叠发言和发言切换与时间压力相关，参与不均与心理负荷相关
- 适用于人机协作、教育评估等真实场景研究
在实验室环境中对语音中的认知负荷估计已有较多研究，但对其在自然协作对话中可靠性的理解仍有限。本文研究语音与互动动态是否能预测双人协作任务中的感知认知负荷。分析了53组受试者完成9项协作任务的音频，提取静态声学、动态及互动特征，使用双头门控循环单元编码器进行训练以预测认知负荷评分。结果表明，对话互动为预测时间压力、心理工作量、努力程度与任务表现相关的认知负荷提供了有效信号。时间需求与发言重叠、说话人切换等轮次动态相关，心理需求则与双方参与不均有关。这些发现强调了任务结构与对话互动在自然协作场景中建模认知负荷的重要性。
原文摘要 · Abstract (English)
Estimating cognitive load from speech has largely been studied in controlled laboratory settings, with limited understanding of its reliability in natural collaborative conversations. We investigate whether speech and interaction dynamics predict perceived cognitive load during dyadic conversations. We analyze audio from 53 dyads performing nine collaborative tasks and extract static acoustic, dynamic, and interaction features to train a two-head Gated Recurrent Unit encoder to predict cognitive load scores. Results show conversational interaction provides useful signals for predicting cognitive load related to time pressure, mental work, effort, and task performance. Temporal demand is associated with turn-taking dynamics such as overlap and speaker switch, while mental demand is linked to imbalanced participation between speakers. These findings highlight the importance of task structure and conversational interaction for modeling cognitive load in natural collaborative settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。