NDLI: AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge

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Automatic Assessment of Depression from Speech and Behavioural Signals

AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge

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AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge

Content Provider	ACM Digital Library
Author	Schuller, Björn Cowie, Roddy Pantic, Maja Almaev, Timur Valstar, Michel Eyben, Florian Smith, Kirsty Krajewski, Jarek
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, arousal and dominance. In addition to structured self-report questionnaires, psychologists and psychiatrists use in their evaluation of a patient's level of depression the observation of facial expressions and vocal cues. It is in this context that we present the fourth Audio-Visual Emotion recognition Challenge (AVEC 2014). This edition of the challenge uses a subset of the tasks used in a previous challenge, allowing for more focussed studies. In addition, labels for a third dimension (Dominance) have been added and the number of annotators per clip has been increased to a minimum of three, with most clips annotated by 5. The challenge has two goals logically organised as sub-challenges: the first is to predict the continuous values of the affective dimensions valence, arousal and dominance at each moment in time. The second is to predict the value of a single self-reported severity of 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	PDF
ISBN	9781450331197
DOI	10.1145/2661806.2661807
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Affective computing Emotion recognition Speech Facial expression Challenge
Content Type	Text
Resource Type	Article

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