Abstract
In this paper, we propose an automatic multimodal approach for inferring interaction occasions (spontaneous or reactive) in multiparty meetings. A variety of features such as head gesture, attention from others, attention towards others, speech tone, speaking time, and lexical cue are integrated. A support vector machines classifier is used to classify interaction occasions based on these features. Our experimental results verified that the proposed approach was really effective, which successfully inferred the occasions of human interactions with a recognition rate by number of 0.870, an accuracy by time of 0.773, and a class average accuracy of 0.812. We also found that the reactive interactions are easier to recognize than the spontaneous ones, and the lexical cue is very important in detecting spontaneous interactions.
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Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (No. 60903125), the Program for New Century Excellent Talents in University (No. NCET-09-0079), and the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2010JM8033).
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Yu, Z., Zhou, X. (2011). Automatic Inference of Interaction Occasion in Multiparty Meetings: Spontaneous or Reactive. In: Park, J., Jin, H., Liao, X., Zheng, R. (eds) Proceedings of the International Conference on Human-centric Computing 2011 and Embedded and Multimedia Computing 2011. Lecture Notes in Electrical Engineering, vol 102. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2105-0_14
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DOI: https://doi.org/10.1007/978-94-007-2105-0_14
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