  Detection of engagement during a conversation is an important function of
human-robot interaction. The level of user engagement can influence the
dialogue strategy of the robot. Our motivation in this work is to detect
several behaviors which will be used as social signal inputs for a real-time
engagement recognition model. These behaviors are nodding, laughter, verbal
backchannels and eye gaze. We describe models of these behaviors which have
been learned from a large corpus of human-robot interactions with the android
robot ERICA. Input data to the models comes from a Kinect sensor and a
microphone array. Using our engagement recognition model, we can achieve
reasonable performance using the inputs from automatic social signal detection,
compared to using manual annotation as input.
