Predicting continuous conflict perception with Bayesian Gaussian processes

Kim, S., Valente, F., Filippone, M. and Vinciarelli, A. (2014) Predicting continuous conflict perception with Bayesian Gaussian processes. IEEE Transactions on Affective Computing, 5(2), pp. 187-200. (doi:10.1109/TAFFC.2014.2324564)

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Publisher's URL: http://dx.doi.org/10.1109/TAFFC.2014.2324564

Abstract

Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach that detects common conversational social signals (loudness, overlapping speech, etc.) and predicts the conflict level perceived by human observers in continuous, non-categorical terms. The proposed regression approach is fully Bayesian and it adopts Automatic Relevance Determination to identify the social signals that influence most the outcome of the prediction. The experiments are performed over the SSPNet Conflict Corpus, a publicly available collection of 1430 clips extracted from televised political debates (roughly 12 hours of material for 138 subjects in total). The results show that it is possible to achieve a correlation close to 0.8 between actual and predicted conflict perception.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Vinciarelli, Professor Alessandro and Filippone, Dr Maurizio
Authors: Kim, S., Valente, F., Filippone, M., and Vinciarelli, A.
Subjects:B Philosophy. Psychology. Religion > BF Psychology
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Research Group:Social Signal Processing
Journal Name:IEEE Transactions on Affective Computing
Publisher:IEEE
ISSN:1949-3045
ISSN (Online):1949-3045
Copyright Holders:Copyright © 2014 IEEE
First Published:First published in IEEE Transactions on Affective Computing 5(2):187-200
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
543881SSPNet - Social Signal Processing Network.Alessandro VinciarelliEuropean Commission (EC)UNSPECIFIEDCOM - COMPUTING SCIENCE
UNSPECIFIEDUNSPECIFIEDInteractive Multimodal Information ManagementAlessandro VinciarelliSwiss National Science FoundationUNSPECIFIEDComputing Science