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

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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