AVEC 2013: the continuous audio/visual emotion and depression recognition challenge

Authors:
Michel Valstar;Björn Schuller;Kirsty Smith;Florian Eyben;Bihan Jiang;Sanjay Bilakhia;Sebastian Schnieder;Roddy Cowie;Maja Pantic
Affiliations:
University of Nottingham, Nottingham, United Kingdom;Technische Universität München, München, Germany;University of Nottingham, Nottingham, United Kingdom;Technische Universität München, München, Germany;Imperial College London, London, United Kingdom;Imperial College London, London, United Kingdom;University of Wuppertal, Wuppertal, Germany;Queen's University Belfast, Belfast, United Kingdom;Imperial College London, London, United Kingdom
Venue:
Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge
Year:
2013

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Abstract

Mood disorders are inherently related to emotion. In particular, the behaviour of people suffering from mood disorders such as unipolar depression shows a strong temporal correlation with the affective dimensions valence and arousal. In addition, psychologists and psychiatrists take the observation of expressive facial and vocal cues into account while evaluating a patient's condition. Depression could result in expressive behaviour such as dampened facial expressions, avoiding eye contact, and using short sentences with flat intonation. It is in this context that we present the third Audio-Visual Emotion recognition Challenge (AVEC 2013). The challenge has two goals logically organised as sub-challenges: the first is to predict the continuous values of the affective dimensions valence and arousal at each moment in time. The second sub-challenge is to predict the value of a single depression indicator for each recording in the dataset. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.