Predicting Listener Backchannels: A Probabilistic Multimodal Approach
IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
Multimodal floor control shift detection
Proceedings of the 2009 international conference on Multimodal interfaces
Multimodal end-of-turn prediction in multi-party meetings
Proceedings of the 2009 international conference on Multimodal interfaces
A multimodal end-of-turn prediction model: learning from parasocial consensus sampling
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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In multi-party meetings, participants need to predict the end of the speaker's utterance and who will start speaking next, and to consider a strategy for good timing to speak next. Gaze behavior plays an important role for smooth turn-taking. This paper proposes a mathematical prediction model that features three processing steps to predict (I) whether turn-taking or turn-keeping will occur, (II) who will be the next speaker in turn-taking, and (III) the timing of the start of the next speaker's utterance. For the feature quantity of the model, we focused on gaze transition patterns near the end of utterance. We collected corpus data of multi party meetings and analyzed how the frequencies of appearance of gaze transition patterns differs depending on situations of (I), (II), and (III). On the basis of the analysis, we construct a probabilistic mathematical model that uses the frequencies of appearance of all participants' gaze transition patterns. The results of an evaluation of the model show the proposed models succeed with high precision compared to ones that do not take gaze transition patterns into account.