研究多人协作中背景语音对打断检测的影响，提出鲁棒方法。
The Impact of Background Speech on Interruption Detection in Collaborative Groups
- 设计抗重叠语音的打断检测方法，适配多组对话场景。
- 发现语言与语调特征能有效标识协作中的打断行为。
- 适合教育AI、课堂互动分析方向的研究者参考。
打断在协作学习中至关重要，影响小组互动与知识建构。当前多数打断检测研究基于单对话、音频清晰的环境，而教室中多组同时讨论导致语音重叠普遍。本文对比单对话与多组对话下的打断检测效果，提出一种对重叠语音具有鲁棒性的新方法，适用于真实课堂部署。研究还揭示了协作互动中打断表现的语言与韵律特征，为未来追踪多组对话中的干扰因素提供基础。
原文摘要 · Abstract (English)
Interruption plays a crucial role in collaborative learning, shaping group interactions and influencing knowledge construction. AI-driven support can assist teachers in monitoring these interactions. However, most previous work on interruption detection and interpretation has been conducted in single-conversation environments with relatively clean audio. AI agents deployed in classrooms for collaborative learning within small groups will need to contend with multiple concurrent conversations -- in this context, overlapping speech will be ubiquitous, and interruptions will need to be identified in other ways. In this work, we analyze interruption detection in single-conversation and multi-group dialogue settings. We then create a state-of-the-art method for interruption identification that is robust to overlapping speech, and thus could be deployed in classrooms. Further, our work highlights meaningful linguistic and prosodic information about how interruptions manifest in collaborative group interactions. Our investigation also paves the way for future works to account for the influence of overlapping speech from multiple groups when tracking group dialog.
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