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Context-Aware Automated Analysis and Annotation of Social HumanAgent Interactions TOBIAS BAUR, GREGOR MEHLMANN, IONUT DAMIAN, FLORIAN LINGENFELSER, ´ JOHANNES WAGNER, BIRGIT LUGRIN, and ELISABETH ANDRE, Augsburg University PATRICK GEBHARD, DFKI GmbH The outcome of interpersonal interactions depends not only on the contents that we communicate verbally, but also on nonverbal social signals. Because a lack of social skills is a common problem for a significant number of people, serious games and other training environments have recently become the focus of research. In this work, we present NovA (Nonverbal behavior Analyzer), a system that analyzes and facilitates the interpretation of social signals automatically in a bidirectional interaction with a conversational agent. It records data of interactions, detects relevant social cues, and creates descriptive statistics for the recorded data with respect to the agent's behavior and the context of the situation. This enhances the possibilities for researchers to automatically label corpora of humanagent interactions and to give users feedback on strengths and weaknesses of their social behavior. Categories and Subject Descriptors: H.1.2 [User/Machine Systems]: Human Information Processing General Terms: Social Signal Processing, Serious Games, Virtual Agents, Affective Computing Additional Key Words and Phrases: Social cue recognition, virtual job interviews, serious
ACM Transactions on Interactive Intelligent Systems (TiiS) – Association for Computing Machinery
Published: Jun 30, 2015
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