A shallow model of backchannel continuers in spoken dialogue
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Natural behavior of a listening agent
Lecture Notes in Computer Science
A probabilistic multimodal approach for predicting listener backchannels
Autonomous Agents and Multi-Agent Systems
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
Multimodal backchannels for embodied conversational agents
IVA'10 Proceedings of the 10th international conference on Intelligent virtual agents
Integrating backchannel prediction models into embodied conversational agents
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
Cultural study on speech duration and perception of virtual agent's nodding
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
Online behavior evaluation with the switching wizard of oz
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
The face speaks: contextual and temporal sensitivity to backchannel responses
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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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In a perception experiment, we systematically varied the quantity, type and timing of backchannels. Participants viewed stimuli of a real speaker side-by-side with an animated listener and rated how human-like they perceived the latter's backchannel behavior. In addition, we obtained measures of appropriateness and optionality for each backchannel from key strokes. This approach allowed us to analyze the influence of each of the factors on entire fragments and on individual backchannels. The originally performed type and timing of a backchannel appeared to be more human-like, compared to a switched type or random timing. In addition, we found that nods are more often appropriate than vocalizations. For quantity, too few or too many backchannels per minute appeared to reduce the quality of the behavior. These findings are important for the design of algorithms for the automatic generation of backchannel behavior for artificial listeners.