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Gesture, Gaze, and Ground

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Machine Learning for Multimodal Interaction (MLMI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3869))

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Abstract

My emphasis in this paper is on floor control in multiparty discourse: the approach is psycholinguistic. This perspective includes turn management, turn exchange and coordination; how to recognize the dominant speaker even when he or she is not speaking, and a theory of all this. The data to be examined comprise a multimodal depiction of a 5-party meeting (a US Air Force war gaming session) and derive from a project carried out jointly with my engineering colleagues, Francis Quek and Mary Harper. See the Chen et al. paper in this volume for details of the recoding session.

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References

  • Chen, L., Rose, R.T., Qiao, Y., Kimbara, I., Parrill, F., Welji, H., Han, T.X., Tu, J., Huang, Z., Harper, M.P., Quek, F., Xiong, Y., McNeill, D., Tuttle, R., Huang, T.: VACE multimodal meeting corpus. In: Renals, S., Bengio, S. (eds.) MLMI 2005. LNCS, vol. 3869, pp. 40–51. Springer, Heidelberg (2006)

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Authors and Affiliations

  1. University of Chicago, USA

    David McNeill

Authors
  1. David McNeill
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Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, Scotland

    Steve Renals

  2. IDIAP Research Institute, Martigny, Switzerland

    Samy Bengio

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© 2006 Springer-Verlag Berlin Heidelberg

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McNeill, D. (2006). Gesture, Gaze, and Ground. In: Renals, S., Bengio, S. (eds) Machine Learning for Multimodal Interaction. MLMI 2005. Lecture Notes in Computer Science, vol 3869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677482_1

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