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From analysis to modeling of engagement as sequences of multimodal behaviors
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From analysis to modeling of engagement as sequences of multimodal behaviors
In this paper, we present an approach to endow an Embodied Conversational Agent with engagement capabilities. We relied on a corpus of expert-novice interactions. Two types of manual annotation were conducted: non-verbal signals such as gestures, head movements and smiles; engagement level of both expert and novice during the interaction. Then, we used a temporal sequence mining algorithm to extract non-verbal sequences eliciting variation of engagement perception. Our aim is to apply these findings in human-agent interaction to analyze user's engagement level and to control agent's behavior. The novelty of this study is to consider explicitly engagement as sequence of multimodal behaviors.
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