AVEC 2016 – Depression, mood, and emotion recognition workshop and challenge

Valstar, Michel F. and Gratch, Jonathan and Schuller, Björn and Ringeval, Fabien and Lalanne, Denis and Torres, Mercedes Torres and Scherer, Stefan and Stratou, Giota and Cowie, Roddy and Pantic, Maja (2016) AVEC 2016 – Depression, mood, and emotion recognition workshop and challenge. In: Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge, 16 Oct 2016, Amsterdam, Netherlands.

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Abstract

The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood and Emotion" will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological depression and emotion analysis, with all participants competing under strictly the same conditions. The goal of the Challenge is to provide a common benchmark test set for multi-modal information processing and to bring together the depression and emotion recognition communities, as well as the audio, video and physiological processing communities, to compare the relative merits of the various approaches to depression and emotion recognition under well-defined and strictly comparable conditions and establish to what extent fusion of the approaches is possible and beneficial. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published in: AVEC '16 : Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge Pages 3-10. Amsterdam, The Netherlands — October 16 - 16, 2016. ACM : New York, ©2016. ISBN: 978-1-4503-4516-3 doi:10.1145/2988257.2988258
Keywords: Affective Computing, Emotion Recognition, Speech, Facial Expression, Physiological signals, Challenge
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Related URLs:
URLURL Type
https://dl.acm.org/citation.cfm?doid=2988257.2988258UNSPECIFIED
Depositing User: Eprints, Support
Date Deposited: 26 Feb 2018 15:03
Last Modified: 01 Mar 2018 06:32
URI: http://eprints.nottingham.ac.uk/id/eprint/50028

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