@inproceedings{75671d789adc4d26b65d29ac2799dea7,
title = "AVEC 2013 - The continuous Audio/Visual Emotion and depression recognition challenge",
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.",
keywords = "Affective computing, Challenge, Emotion recognition, Facial expression, Speech",
author = "Michel Valstar and Bj{\"o}rn Schuller and Kirsty Smith and Florian Eyben and Bihan Jiang and Sanjay Bilakhia and Sebastian Schnieder and Roddy Cowie and Maja Pantic",
year = "2013",
doi = "10.1145/2512530.2512533",
language = "English",
isbn = "9781450323956",
series = "AVEC 2013 - Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge",
publisher = "Association for Computing Machinery",
pages = "3--10",
booktitle = "AVEC 2013 - Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge",
note = "3rd ACM International Workshop on Audio/Visual Emotion Challenge, AVEC 2013 ; Conference date: 21-10-2013 Through 21-10-2013",
}