The Impact of Background Speech on Interruption Detection in Collaborative Groups
This addresses the challenge for AI agents in monitoring group interactions in noisy educational settings, representing an incremental improvement by adapting existing techniques to multi-conversation environments.
The paper tackles the problem of detecting interruptions in collaborative learning groups where overlapping speech from multiple conversations complicates audio analysis, and presents a state-of-the-art method robust to such conditions for potential classroom deployment.
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.