Prosodic cues to discourse boundaries in experimental dialogues
Speech Communication
ELIZA—a computer program for the study of natural language communication between man and machine
Communications of the ACM
Communication and prosody: functional aspects of prosody
Speech Communication - Dialogue and prosody
Predicting Listener Backchannels: A Probabilistic Multimodal Approach
IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Learning a model of speaker head nods using gesture corpora
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
A probabilistic multimodal approach for predicting listener backchannels
Autonomous Agents and Multi-Agent Systems
Proceedings of the Workshop on Use of Context in Vision Processing
Parasocial consensus sampling: combining multiple perspectives to learn virtual human behavior
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Latent mixture of discriminative experts for multimodal prediction modeling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Concensus of self-features for nonverbal behavior analysis
HBU'10 Proceedings of the First international conference on Human behavior understanding
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
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If a dialog system can respond to a user as naturally as a human, the interaction will be smoother. In this research, we aim to develop a dialog system by emulating the human behavior in a chat-like dialog. In this paper, we developed a dialog system which could generate chat-like responses and their timing using a decision tree. The system could perform "collaborative completion," "aizuchi" (back-channel) and so on. The decision tree utilized the pitch and the power contours of user's utterance, recognition hypotheses, and response preparation status of the response generator, at every time segment as features to generate response timing.