Talker and team dependent modeling techniques for intelligent interruption interfaces

Abstract

The Collaborative Communication Interruption Management System or C-CIMS [1] uses machine learning techniques to build task boundary inference models to send interruptions at appropriate times within distributed multi-user, multitasking interactions. The primary objective of this work is to explore improving C-CIMS performance using speaker and team dependent machine learning techniques. This has the potential to optimize system performance for each talker or team engaged in the interaction. An analysis of variance illustrated that there is a significant difference in C-CIMS performance using the talker-dependent models compared to the team-dependent models. Additionally a subset of talker and teams significantly outperform the baseline model. These results motivate the continued exploration of additional techniques to maximize C-CIMS performance in making improved accurate decisions in disseminating interruptions.