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
Logarithmic opinion pools for conditional random fields
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Creating Rapport with Virtual Agents
IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
Predicting Listener Backchannels: A Probabilistic Multimodal Approach
IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
A spoken dialog system for chat-like conversations considering response timing
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
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
IVA'06 Proceedings of the 6th 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
Perception markup language: towards a standardized representation of perceived nonverbal behaviors
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
Speaker-adaptive multimodal prediction model for listener responses
Proceedings of the 15th ACM on International conference on multimodal interaction
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In many computational linguistic scenarios, training labels are subjectives making it necessary to acquire the opinions of multiple annotators/experts, which is referred to as "wisdom of crowds". In this paper, we propose a new approach for modeling wisdom of crowds based on the Latent Mixture of Discriminative Experts (LMDE) model that can automatically learn the prototypical patterns and hidden dynamic among different experts. Experiments show improvement over state-of-the-art approaches on the task of listener backchannel prediction in dyadic conversations.