AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge

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.

[1]  Björn W. Schuller,et al.  The INTERSPEECH 2009 emotion challenge , 2009, INTERSPEECH.

[2]  Mohammad H. Mahoor,et al.  Social risk and depression: Evidence from manual and automatic facial expression analysis , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[3]  Michel F. Valstar,et al.  Automatic Behaviour Understanding in Medicine , 2014, RFMIR '14.

[4]  Sascha Meudt,et al.  Fusion of Audio-visual Features using Hierarchical Classifier Systems for the Recognition of Affective States and the State of Depression , 2014, ICPRAM.

[5]  Björn W. Schuller,et al.  The INTERSPEECH 2010 paralinguistic challenge , 2010, INTERSPEECH.

[6]  Vladimir Pavlovic,et al.  Dynamic Probabilistic CCA for Analysis of Affective Behaviour , 2012, ECCV.

[7]  Michel F. Valstar,et al.  Local Gabor Binary Patterns from Three Orthogonal Planes for Automatic Facial Expression Recognition , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[8]  Jude Stansfield.  Improving the mental health of the population: a strategy for Europe , 2006 .

[9]  Thomas F. Quatieri,et al.  Vocal biomarkers of depression based on motor incoordination , 2013, AVEC@ACM Multimedia.

[10]  Dirk Heylen,et al.  Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing , 2012, IEEE Transactions on Affective Computing.

[11]  M. Posternak,et al.  A Review of Studies of the Hamilton Depression Rating Scale in Healthy Controls: Implications for the Definition of Remission in Treatment Studies of Depression , 2004, The Journal of nervous and mental disease.

[12]  M. First,et al.  Structured clinical interview for DSM-IV axis I disorders : SCID-I: clinical version : administration booklet , 1996 .

[13]  Jeffrey F. Cohn,et al.  Detecting Depression Severity from Vocal Prosody , 2013, IEEE Transactions on Affective Computing.

[14]  Albert A. Rizzo,et al.  Automatic behavior descriptors for psychological disorder analysis , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[15]  K. Scherer,et al.  The World of Emotions is not Two-Dimensional , 2007, Psychological science.

[16]  E. Vesterinen,et al.  Affective Computing , 2009, Encyclopedia of Biometrics.

[17]  Heng Wang,et al.  Depression recognition based on dynamic facial and vocal expression features using partial least square regression , 2013, AVEC@ACM Multimedia.

[18]  Subhransu Maji,et al.  Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Vladimir Pavlovic,et al.  Dynamic Probabilistic CCA for Analysis of Affective Behavior and Fusion of Continuous Annotations , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Björn Schuller,et al.  Opensmile: the munich versatile and fast open-source audio feature extractor , 2010, ACM Multimedia.

[21]  Björn W. Schuller,et al.  AVEC 2013: the continuous audio/visual emotion and depression recognition challenge , 2013, AVEC@ACM Multimedia.

[22]  JOSEPH ZILBER,et al.  issue 1 , 2020, JORDANIAN JOURNAL OF ENGINEERING AND CHEMICAL INDUSTRIES (JJECI).

[23]  Björn W. Schuller,et al.  Real-life voice activity detection with LSTM Recurrent Neural Networks and an application to Hollywood movies , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[24]  Michel F. Valstar,et al.  Distribution-based iterative pairwise classification of emotions in the wild using LGBP-TOP , 2013, ICMI '13.

[25]  Roddy Cowie,et al.  FEELTRACE: an instrument for recording perceived emotion in real time , 2000 .

[26]  M. Hamilton,et al.  Development of a rating scale for primary depressive illness. , 1967, The British journal of social and clinical psychology.

[27]  Björn W. Schuller,et al.  OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[28]  Rosalind W. Picard.  Affective computing: (526112012-054) , 1997 .

[29]  R. Gur,et al.  Automated Facial Action Coding System for dynamic analysis of facial expressions in neuropsychiatric disorders , 2011, Journal of Neuroscience Methods.

[30]  R. Bagby,et al.  The Hamilton Depression Rating Scale: has the gold standard become a lead weight? , 2004, The American journal of psychiatry.

[31]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[32]  R. Gur,et al.  Automated video-based facial expression analysis of neuropsychiatric disorders , 2008, Journal of Neuroscience Methods.

[33]  Fernando De la Torre,et al.  Detecting depression from facial actions and vocal prosody , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[34]  A. Beck,et al.  Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. , 1996, Journal of personality assessment.