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Automatic detection of group functional roles in face to face interactions

Published: 02 November 2006 Publication History

Abstract

In this paper, we discuss a machine learning approach to automatically detect functional roles played by participants in a face to face interaction. We shortly introduce the coding scheme we used to classify the roles of the group members and the corpus we collected to assess the coding scheme reliability as well as to train statistical systems for automatic recognition of roles. We then discuss a machine learning approach based on multi-class SVM to automatically detect such roles by employing simple features of the visual and acoustical scene. The effectiveness of the classification is better than the chosen baselines and although the results are not yet good enough for a real application, they demonstrate the feasibility of the task of detecting group functional roles in face to face interactions.

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cover image ACM Conferences
ICMI '06: Proceedings of the 8th international conference on Multimodal interfaces
November 2006
404 pages
ISBN:159593541X
DOI:10.1145/1180995
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 November 2006

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Author Tags

  1. group interaction
  2. intelligent environments
  3. support vector machines

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  • (2020)A Multi-Stream Recurrent Neural Network for Social Role Detection in Multiparty InteractionsIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2020.299239414:3(554-567)Online publication date: Mar-2020
  • (2020)Leader Identification Using Multimodal Information in Multi-party Conversations2020 International Conference on Asian Language Processing (IALP)10.1109/IALP51396.2020.9310465(7-12)Online publication date: 4-Dec-2020
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