Online analysis of hierarchical events in meetings

Authors:
Xiang Zhang;Guang-You Xu;Xiao-Ling Xiao;Lin-Mi Tao
Affiliations:
Department of Computer Science, Tsinghua University, Beijing, China and Yangtze University, Jinzhou, Hubei, China;Department of Computer Science, Tsinghua University, Beijing, China;Department of Computer Science and Technology, Wuhan University of Technology, Wuhan, China;Department of Computer Science, Tsinghua University, Beijing, China
Venue:
UI-HCII'07 Proceedings of the 2nd international conference on Usability and internationalization
Year:
2007

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Abstract

Automatic online analysis of meetings is very important from three points of view: serving as an important archive of a meeting, understanding human interaction processes, and providing the attentive services based on the meeting situation for participants. Based on this view, this paper presents principle and implementation of online analysis of hierarchical events in meeting scenario. A hierarchical dynamic Bayesian network modeling different levels of events is designed. In this model, the recognition of low-level events is supervised by high-level events Rao-Blackwellized particle filter is proposed for on-line inference for the hierarchical dynamic Bayesian network. Situation events and four sorts of interaction events in meeting scenario are detected and recognized. Experimental results show that our approach can detect and recognize multi-layer semantic events in dynamic environment. Comparing with previous methods of meeting analysis, our approach supports online probabilistic inference for activities at different layers in meeting scenario.