Mining a Multimodal Corpus for Non-Verbal Behavior Sequences Conveying Attitudes

Chollet, M. , Ochs, M. and Pelachaud, C. (2014) Mining a Multimodal Corpus for Non-Verbal Behavior Sequences Conveying Attitudes. In: 9th International Conference on Language Resources and Evaluation (LREC '14), Reykjavik, Iceland, 26-31 May 2014, pp. 3417-3424. ISBN 9782951740884

Abstract

Interpersonal attitudes are expressed by non-verbal behaviors on a variety of different modalities. The perception of these behaviors is influenced by how they are sequenced with other behaviors from the same person and behaviors from other interactants. In this paper, we present a method for extracting and generating sequences of non-verbal signals expressing interpersonal attitudes. These sequences are used as part of a framework for non-verbal expression with Embodied Conversational Agents that considers different features of non-verbal behavior: global behavior tendencies, interpersonal reactions, sequencing of non-verbal signals, and communicative intentions. Our method uses a sequence mining technique on an annotated multimodal corpus to extract sequences characteristic of different attitudes. New sequences of non-verbal signals are generated using a probabilistic model, and evaluated using the previously mined sequences.

• Item Type: Conference Proceedings
• Additional Information: This research has been partially supported by the European Community Seventh Framework Program (FP7/2007-2013), under grant agreement no. 288578 (TARDIS).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Chollet, Dr Mathieu
• Authors: Chollet, M., Ochs, M., and Pelachaud, C.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014
• ISBN: 9782951740884

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