Natural behavior of a listening agent
Lecture Notes in Computer Science
SmartBody: behavior realization for embodied conversational agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Creating Rapport with Virtual Agents
IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
The effect of affective iconic realism on anonymous interactants' self-disclosure
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Coordination in conversation and rapport
EmbodiedNLP '07 Proceedings of the Workshop on Embodied Language Processing
Can virtual humans be more engaging than real ones?
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
A spoken dialog system for chat-like conversations considering response timing
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
ZiF'06 Proceedings of the Embodied communication in humans and machines, 2nd ZiF research group international conference on Modeling communication with robots and virtual humans
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
Evaluating models of speaker head nods for virtual agents
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Backchannel strategies for artificial listeners
IVA'10 Proceedings of the 10th international conference on Intelligent virtual agents
Learning backchannel prediction model from parasocial consensus sampling: a subjective evaluation
IVA'10 Proceedings of the 10th international conference on Intelligent virtual agents
Learning and evaluating response prediction models using parallel listener consensus
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
The multiLis corpus - dealing with individual differences in nonverbal listening behavior
Proceedings of the Third COST 2102 international training school conference on Toward autonomous, adaptive, and context-aware multimodal interfaces: theoretical and practical issues
Modeling wisdom of crowds using latent mixture of discriminative experts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
A multimodal end-of-turn prediction model: learning from parasocial consensus sampling
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
IVA'11 Proceedings of the 10th international conference on Intelligent virtual agents
Appropriate and inappropriate timing of listener responses from multiple perspectives
IVA'11 Proceedings of the 10th international conference on Intelligent virtual agents
Computational study of human communication dynamic
J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Understanding communicative emotions from collective external observations
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Speaker-adaptive multimodal prediction model for listener responses
Proceedings of the 15th ACM on International conference on multimodal interaction
Timing and entrainment of multimodal backchanneling behavior for an embodied conversational agent
Proceedings of the 15th ACM on International conference on multimodal interaction
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Virtual humans are embodied software agents that should not only be realistic looking but also have natural and realistic behaviors. Traditional virtual human systems learn these interaction behaviors by observing how individuals respond in face-to-face situations (i.e., dir ect interaction). In contrast, this paper introduces a novel methodological approach called paras ocial consensus sampling (PCS) which allows multiple individuals to vicariously experience the same situation to gain insight on the typical (i.e., consensus view) of human responses in social interaction. This approach can help tease a part what is idiosyncratic from what is essential and help reveal the strength of cues that elicit social responses. Our PCS approach has several advantages over traditional methods: (1) it integrates data from multiple independent listeners interacting with the same speaker, (2) it associates probability of how likely feedback will be given over time, (3) it can be used as a prior to analyze and understand the face-to-face interaction data, (4) it facilitates much quicker and cheaper data collection. In this paper, we apply our PCS approach to learn a predictive model of listener backchannel feedback. Our experiments demonstrate that a virtual human driven by our PCS approach creates significantly more rapport and is perceived as more believable than the virtual human driven by face-to-face interaction data.