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.
Recognizing users' engagement state and intentions is a pressing task for computational agents to facilitate fluid conversations in situated interactions. We investigate how to quantitatively evaluate high-level user engagement and intentions based on low-level visual cues, and how to design engagement-aware behaviors for the conversational agents to behave in a sociable manner. Drawing on machine learning techniques, we propose two computational models to quantify users' attention saliency and engagement intentions. Their performances are validated by a close match between the predicted values and the ground truth annotation data. Next, we design a novel engagement-aware behavior model for the agent to adjust its direction of attention and manage the conversational floor based on the estimated users' engagement. In a user study, we evaluated the agent's behaviors in a multiparty dialog scenario. The results show that the agent's engagement-aware behaviors significantly improved the effectiveness of communication and positively affected users' experience.  We address the annotation of engagement in the context of human-machine interaction. Engagement represents the level of how much a user is being interested in and willing to continue the current interaction. The conversational data used in the annotation work is a human-robot interaction corpus where a human subject talks with the android ERICA, which is remotely operated by another human subject. The annotation work was done by multiple third-party annotators, and the task was to detect the time point when the level of engagement becomes high. The annotation results indicate that there are agreements among the annotators although the numbers of annotated points are different among them. It is also found that the level of engagement is related to turn-taking behaviors. Furthermore, we conducted interviews with the annotators to reveal behaviors used to show a high level of engagement. The results suggest that laughing, backchannels and nodding are related to the level of engagement.