research-article

AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition

Authors:

Fabien Ringeval,

Björn Schuller,

Michel Valstar,

Nicholas Cummins,

Maximilian Schmitt,

Shahin Amiriparian,

Eva-Maria Messner,

Adria Mallol-Ragolta,

Mohammad Soleymani,

Maja PanticAuthors Info & Claims

AVEC '19: Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop

Pages 3 - 12

https://doi.org/10.1145/3347320.3357688

Published: 15 October 2019 Publication History

Abstract

The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) 'State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition' is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively.

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Index Terms

AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
1. General and reference
  1. Cross-computing tools and techniques
    1. Performance

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cover image ACM Conferences

AVEC '19: Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop

October 2019

96 pages

ISBN:9781450369138

DOI:10.1145/3347320

General Chairs:
Fabien Ringeval
Grenoble Alps University, France
,
Björn Schuller
University of Augsburg/Imperial College London, Germany/UK
,
Michel Valstar
University of Nottingham, UK
,
Nicholas Cummins
University of Augsburg, Germany
,
Roddy Cowie
Queen's University Belfast, UK
,
Maja Pantic
Imperial College London/Twente University, UK/The Netherlands

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Fu CQian FSu YSu KSong SNiu MShi JLiu ZLiu CIshi CIshiguro H(2025)Facial action units guided graph representation learning for multimodal depression detectionNeurocomputing10.1016/j.neucom.2024.129106619(129106)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129106
Kamińska DKamińska OSochacka MSokół-Szawłowska M(2024)The Role of Selected Speech Signal Characteristics in Discriminating Unipolar and Bipolar DisordersSensors10.3390/s2414472124:14(4721)Online publication date: 20-Jul-2024
https://doi.org/10.3390/s24144721
Khoo LLim MChong CMcNaney R(2024)Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing ApproachesSensors10.3390/s2402034824:2(348)Online publication date: 6-Jan-2024
https://doi.org/10.3390/s24020348
Li XDong YYi YLiang ZYan S(2024)Hypergraph Neural Network for Multimodal Depression RecognitionElectronics10.3390/electronics1322454413:22(4544)Online publication date: 19-Nov-2024
https://doi.org/10.3390/electronics13224544
Ankur Binwal (2024)Emotional Intelligence in Voice Assistants : Advancing Human-AI InteractionInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT24105103910:5(513-523)Online publication date: 9-Oct-2024
https://doi.org/10.32628/CSEIT241051039
Ankur Binwal (2024)Emotional Intelligence in Voice Assistants : Advancing Human-AI InteractionInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT24105102010:5(449-460)Online publication date: 9-Oct-2024
https://doi.org/10.32628/CSEIT241051020
Shidara KTanaka HAdachi HKanayama DKudo TNakamura S(2024)Adapting the Number of Questions Based on Detected Psychological Distress for Cognitive Behavioral Therapy With an Embodied Conversational Agent: Comparative StudyJMIR Formative Research10.2196/500568(e50056)Online publication date: 14-Mar-2024
https://doi.org/10.2196/50056
TENG SLIU JHUANG YCHAI STATEYAMA THUANG XLIN LCHEN Y(2024)An Intra- and Inter-Emotion Transformer-Based Fusion Model with Homogeneous and Diverse Constraints Using Multi-Emotional Audiovisual Features for Depression DetectionIEICE Transactions on Information and Systems10.1587/transinf.2023HCP0006E107.D:3(342-353)Online publication date: 1-Mar-2024
https://doi.org/10.1587/transinf.2023HCP0006
Wu PXu FLin H(2024)Enhanced Depression Detection through Optimally Weighted Spectrogram Feature FusionProceedings of the 2024 13th International Conference on Computing and Pattern Recognition10.1145/3704323.3704375(226-232)Online publication date: 25-Oct-2024
https://dl.acm.org/doi/10.1145/3704323.3704375
Pillai ANepal SWang WNemesure MHeinz MPrice GLekkas DCollins AGriffin TBuck BPreum SCohen TJacobson NBen-Zeev DCampbell A(2024)Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile PhonesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314527:4(1-38)Online publication date: 12-Jan-2024
https://doi.org/10.1145/3631452
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