Chollet, M. , Ochs, M. and Pelachaud, C. (2019) A methodology for the automatic extraction and generation of non-verbal signals sequences conveying interpersonal attitudes. IEEE Transactions on Affective Computing, 10(4), pp. 585-598. (doi: 10.1109/TAFFC.2017.2753777)
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Abstract
In many applications, Embodied Conversational Agents (ECAs) must be able to express various affects such as emotions or social attitudes. Non-verbal signals, such as smiles or gestures, contribute to the expression of attitudes. Social attitudes affect the whole behavior of a person: they are “characteristic of an affective style that colors the entire interaction” [1] . Moreover, recent findings have demonstrated that non-verbal signals are not interpreted in isolation but along with surrounding signals. Non-verbal behavior planning models designed to allow ECAs to express attitudes should thus consider complete sequences of non-verbal signals and not only signals independently of one another. However, existing models do not take this into account, or in a limited manner. The contribution of this paper is a methodology for the automatic extraction of sequences of non-verbal signals characteristic of a social phenomenon from a multimodal corpus, and a non-verbal behavior planning model that takes into account sequences of non-verbal signals rather than signals independently. This methodology is applied to design a virtual recruiter capable of expressing social attitudes, which is then evaluated in and out of an interaction context.
Item Type: | Articles |
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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: | IEEE Transactions on Affective Computing |
Publisher: | IEEE |
ISSN: | 1949-3045 |
ISSN (Online): | 1949-3045 |
Published Online: | 18 September 2017 |
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