Occasionally, participants in a meeting can leave with different understandings of what had been discussed. For meetings that require immediate response (such as disaster response planning), the participants must share a common understanding of the decisions reached by the group to ensure successful execution of their mission. In such domains, inconsistency among individuals' understanding of the meeting results would be detrimental, as this can potentially degrade group performance. Thus, detecting the occurrence of inconsistencies in understanding among meeting participants is a desired capability for an intelligent system that would monitor meetings and provide feedback to spur stronger group understanding.      

In this paper, we seek to predict the consistency among team members' understanding of group decisions. We use self-reported summaries as a representative measure for team members' understanding following meetings, and present a computational model that uses a set of verbal and nonverbal features from natural dialogue. This model focuses on the conversational dynamics between the participants, rather than on what is being discussed.  We apply our model to a real-world conversational dataset and show that its features can predict group consistency with greater accuracy than conventional dialogue features. We also show that the combination of verbal and nonverbal features in multimodal fusion improves several performance metrics, and that our results are consistent across different meeting phases.