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
Computers in Human Behavior
Talking bodies: Sensitivity to desynchronization of conversations
ACM Transactions on Applied Perception (TAP)
A probabilistic multimodal approach for predicting listener backchannels
Autonomous Agents and Multi-Agent Systems
The saliency of anomalies in animated human characters
ACM Transactions on Applied Perception (TAP)
Backchannel strategies for artificial listeners
IVA'10 Proceedings of the 10th international conference on Intelligent virtual agents
IVA'11 Proceedings of the 10th international conference on Intelligent virtual agents
Backchannels: quantity, type and timing matters
IVA'11 Proceedings of the 10th international conference on Intelligent virtual agents
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Advances in animation and sensor technology allow us to engage in face-to-face conversations with virtual agents [1]. One major challenge is to generate the virtual agent's appropriate, human-like behavior contingent with that of the human conversational partner. Models of (nonverbal) behavior are pre-dominantly learned from corpora of dialogs between human subjects [2], or based on simple observations from literature (e.g. [3,4,5,6])