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AVEC 2013: the continuous audio/visual emotion and depression recognition challenge
| Content Provider | ACM Digital Library |
|---|---|
| Author | Schuller, Björn Cowie, Roddy Bilakhia, Sanjay Pantic, Maja Jiang, Bihan Valstar, Michel Eyben, Florian Schnieder, Sebastian Smith, Kirsty |
| 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. |
| Starting Page | 3 |
| Ending Page | 10 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450323956 |
| DOI | 10.1145/2512530.2512533 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-10-21 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Affective computing Emotion recognition Depression |
| Content Type | Text |
| Resource Type | Article |
