Learning to predict engagement with a spoken dialog system in open-world settings
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Estimating user's engagement from eye-gaze behaviors in human-agent conversations
Proceedings of the 15th international conference on Intelligent user interfaces
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Two people walk into a bar: dynamic multi-party social interaction with a robot agent
Proceedings of the 14th ACM international conference on Multimodal interaction
Furhat: a back-projected human-like robot head for multiparty human-machine interaction
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
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A social agent such as a receptionist or an escort robot encounters challenges when communicating with people in open areas. The agent must know not to react to distracting acoustic and visual events and it needs to appropriately handle situations that include multiple humans, being able to to focus on active interlocutors and appropriately shift attention based on the context. We describe a multiparty interaction agent that helps multiple users arrange a common activity. From the user study we conducted, we found that the agent can discriminate between active and inactive interlocutors well by using the skeletal and azimuth information. Participants found the addressee much clearer when an animated talking head was used.