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2017
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(2017) : Turn-taking intention recognition using multimodal cues in social human-robot interaction: 2017 17th International Conference on Control, Automation and Systems (ICCAS): Jeju, Korea: IEEE, S. 1300-1302
DOI: https://doi.org/10.23919/ICCAS.2017.8204407 Abstract: Turn-taking is an essential social skill for human communication. The robot needs to recognize the end of turn for timely response to the user with little delay in human-robot interaction. In this paper, we propose a turn-taking intention recognition system that determine the timing of turn-taking using multimodal cues in social Human-Robot Interaction (sHRI). In order to evaluate the turn-taking intention recognition system, we collect multimodal data set and conducted experiments. To that end, we designed a human-robot interaction scenario including turn-taking and conducted an experiment with 30 participants using the humanoid robot NAO. In experiments, we validate recognition models trained multimodal dataset by machine learning methods.
Keywords: Encoding, essential social skill, Floors, human communication, humanoid robot NAO, Humanoid Robots, human-robot interaction, ieee xplore, Künstliche Intelligenz, learning (artificial intelligence), Lips, machine learning methods, multimodal cues, multimodal dataset, Robots, sHRI, social human-robot interaction, Social robot, speech, timing, Turn-Taking, turn-taking intention recognition system
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