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Discovering Group Nonverbal Conversational Patterns with Topics AbstractThis paper addresses the problem of discovering conversational group dynamics from nonverbal cues extracted from thin-slices of interaction. We first propose and analyze a novel thin-slice interaction descriptor - a bag of group nonverbal patterns - which robustly captures the turn-taking behavior of the members of a group while integrating its leader’s position. We then rely on probabilistic topic modeling of the interaction descriptors which, in a fully unsupervised way, is able to discover group interaction patterns that resemble prototypical leadership styles proposed in social psychology. Our method, validated on the Augmented Multi-Party Interaction (AMI) meeting corpus, facilitates the retrieval of group conversational segments where semantically meaningful group behaviours emerge.
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