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Predicting Turn-Taking by Compact Gazing Transition Patterns in Multiparty Conversation

  • Li Tian
  • Qi Jia
  • Zhen Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)

Abstract

Gaze behavior plays an important role for analyzing turn-taking in multiparty conversation. In this study, we propose a general and powerful model for predicting turn-taking by analyzing gaze transition patterns in four-participant conversation. We propose gaze labels of different speaker’s and listener’s gaze movements and then code every gaze transition pattern to a two-label pattern. After that, we analyze the gaze transition patterns by quantitative analysis to confirm their effectiveness. Finally, we build up a prediction model for predicting turn-taking based on these gaze transition patterns. Experiments demonstrate that the prediction results obtained by our model are superior to the state-of-the-art.

Keywords

Multiparty conversation Gaze behavior analysis Turn-taking Nonverbal behaviors Gaze transition pattern 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Foshan UniversityFoshanChina
  2. 2.South China University of TechnologyGuangzhouChina

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