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A novel probabilistic framework is proposed for inferring the structure of conversation in face-to-face multiparty communication, based on gaze patterns, head directions, and the presence/absence of utterances. First, as the structure of conversation, this study focuses on the combination of participants and their participation roles. Next, we hypothesize that the structure of conversation governs how people behave during conversation, and propose a conversation model based on the Markov-switching model, a kind of dynamic Bayesian network. In this model, the state of the high-level process, we call it the conversation regime, is assumed to correspond to the conversation structure and that its changes over time exhibit Markov properties. Also, the conversation regime controls the dynamics of utterances and gaze patterns, which stochastically yield measurable head directions. The conversation regimes are hypothetically configured based on typical structures exhibited by gaze patterns among the participants during conversations. Furthermore, a Markov chain Monte Carlo method called the Gibbs sampler is used to realize the Bayesian estimation of conversation regime, gaze pattern, and model parameters from the observed sequential data of head directions and utterances. Finally, experiments on four-person conversations confirm the effectiveness of the proposed framework in estimating gaze directions and conversation regimes.","subitem_description_type":"Abstract"}]},"item_10_identifier_60":{"attribute_name":"URI","attribute_value_mlt":[{"subitem_identifier_type":"HDL","subitem_identifier_uri":"http://hdl.handle.net/2237/10281"}]},"item_10_publisher_32":{"attribute_name":"\u51fa\u7248\u8005","attribute_value_mlt":[{"subitem_publisher":"\u60c5\u5831\u51e6\u7406\u5b66\u4f1a"}]},"item_10_rights_12":{"attribute_name":"\u6a29\u5229","attribute_value_mlt":[{"subitem_rights":"\u3053\u3053\u306b\u63b2\u8f09\u3057\u305f\u8457\u4f5c\u7269\u306e\u5229\u7528\u306b\u95a2\u3059\u308b\u6ce8\u610f \u672c\u8457\u4f5c\u7269\u306e\u8457\u4f5c\u6a29\u306f\uff08\u793e\uff09\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u306b\u5e30\u5c5e\u3057\u307e\u3059\u3002\u672c\u8457\u4f5c\u7269\u306f\u8457\u4f5c\u6a29\u8005\u3067\u3042\u308b\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u306e\u8a31\u53ef\u306e\u3082\u3068\u306b\u63b2\u8f09\u3059\u308b\u3082\u306e\u3067\u3059\u3002\u3054\u5229\u7528\u306b\u5f53\u305f\u3063\u3066\u306f\u300c\u8457\u4f5c\u6a29\u6cd5\u300d\u306a\u3089\u3073\u306b\u300c\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u502b\u7406\u7db1\u9818\u300d\u306b\u5f93\u3046\u3053\u3068\u3092\u304a\u9858\u3044\u3044\u305f\u3057\u307e\u3059\u3002 Notice for the use of this material The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). 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