The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
AVEC 2014: 3D Dimensional Affect 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, 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Development of a Rating Scale for Primary Depressive Illness
HAMILTON, M. (1967) · British Journal of Social and Clinical Psychology · doi:10.1111/j.2044-8260.1967.tb00530.x · Cited by 8,410
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382
Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients
Beck, A. T., Steer, R. A., Ball, R., & Ranieri, W. F. (1996) · Journal of Personality Assessment · doi:10.1207/s15327752jpa6703_13 · Cited by 5,965
Opensmile: the munich versatile and fast open-source audio feature extractor
Eyben, F., Wöllmer, M., & Schuller, B. (2010) · Proceedings of the 18th ACM international conference on Multimedia · doi:10.1145/1873951.1874246 · Cited by 2,642
Self-citationThe world of emotions is not two-dimensional
Fontaine, J. R. J., Scherer, K. R., Roesch, E. B., & Ellsworth, P. C. (2007) · Psychological Science · doi:10.1111/j.1467-9280.2007.02024.x · Cited by 1,179
The Hamilton Depression Rating Scale: Has the Gold Standard Become a Lead Weight?
Bagby, R. M., Ryder, A. G., Schuller, D. R., & Marshall, M. B. (2004) · American Journal of Psychiatry · doi:10.1176/appi.ajp.161.12.2163 · Cited by 1,148
Classification using intersection kernel support vector machines is efficient
Maji, S., Berg, A. C., & Malik, J. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587630 · Cited by 940
The INTERSPEECH 2009 emotion challenge
Schuller, B., Steidl, S., & Batliner, A. (2009) · Interspeech 2009 · doi:10.21437/interspeech.2009-103 · Cited by 938
Self-citationAVEC 2013: the continuous audio/visual emotion and depression recognition challenge
Valstar, M., Schuller, B., Smith, K., Eyben, F., Jiang, B., Bilakhia, S., Schnieder, S., Cowie, R., & Pantic, M. (2013) · Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge · doi:10.1145/2512530.2512533 · Cited by 581
Self-citation
Cited by
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338
Hollis, C., Falconer, C. J., Martin, J. L., Whittington, C., Stockton, S., Glazebrook, C., & Davies, E. B. (2017) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12663 · Cited by 846
AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge
Valstar, M., Gratch, J., Schuller, B., Ringeval, F., Lalanne, D., Torres Torres, M., Scherer, S., Stratou, G., Cowie, R., & Pantic, M. (2016) · Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge · doi:10.1145/2988257.2988258 · Cited by 503
Self-citationNatural language processing applied to mental illness detection: a narrative review
Zhang, T., Schoene, A. M., Ji, S., & Ananiadou, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00589-7 · Cited by 476
Deep learning in mental health outcome research: a scoping review
Su, C., Xu, Z., Pathak, J., & Wang, F. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0780-3 · Cited by 368
Metadata
- Authors
- Valstar, M., Schuller, B., Smith, K., Almaev, T., Eyben, F., Krajewski, J., Cowie, R., & Pantic, M.
- Venue
- Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge
- Publisher
- ACM
- Published
- 2014-11-07
- Pages
- 3-10
- OpenAlex
- W2056403322
- Cited by
- 472
- References
- 31
- FWCI
- 62.34
- Type
- conference paper
- Language
- fr
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge. https://app.cochat.ai/papers/10.1145/2661806.2661807
- The paper
- Valstar, M., Schuller, B., Smith, K., Almaev, T., Eyben, F., Krajewski, J., Cowie, R., & Pantic, M. (2014). AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge. Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge. https://doi.org/10.1145/2661806.2661807
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
