AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
Open accessAbstract
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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Speech contains rich information that can infer human mental and physical health.
“Speech, as one of the dominant sounds, contains rich information that can infer human mental health and physical health [34,39].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [39]
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References 78 · Cited by 409
Research works printed but not linked: 10
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- [62][62] Björn Schuller, Michel Valstar, Florian Eyben, Roddy Cowie, and Maja Pantic. 2012. AVEC 2012 - The continuous Audio/Visual Emotion Challenge. In Proc. 14th ACM International Conference on Multimodal Interaction (ICMI) . ACM, Santa Monica, CA, USA, 449-456. -
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- [75][75] Michel Valstar, Björn Schuller, Jarek Krajewski, Roddy Cowie, and Maja Pantic. 2014. AVEC 2014: The 4th international Audio/Visual Emotion Challenge and workshop. In Proc. 22nd ACM International Conference on Multimedia (ACM MM) . ACM, Orlando, FL, USA, 1243-1244. -
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- Authors
- Ringeval, F., Schuller, B., Valstar, M., Cummins, N., Cowie, R., Tavabi, L., Schmitt, M., Alisamir, S., Amiriparian, S., Messner, E.-M., Song, S., Liu, S., Zhao, Z., Mallol-Ragolta, A., Ren, Z., Soleymani, M., & Pantic, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- University of Augsburg (DE)
- University of Nottingham (GB)
- Queen's University Belfast (GB)
- University of Southern California (US)
- Association for Computing Machinery (US)
- Universität Ulm (DE)
- Tianjin Normal University (CN)
- Imperial College London & University of Twente, London, United Kingdom
- ORCID
- Venue
- Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop
- Publisher
- ACM
- Published
- 2019-10-15
- Pages
- 3-12
- OpenAlex
- W2965317148
- Cited by
- 409
- References
- 78
- Citations by year
- 2026: 68 · 2025: 78 · 2024: 66 · 2023: 44 · 2022: 77 · 2021: 34 · 2020: 32 · 2019: 10
- FWCI
- 48.23
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- University of Twente Research Information (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OPUS (Augsburg University) (submittedVersion)
- University of Twente Research Information (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Federation of Pharmaceutical Industries and Associations
- European Commission (115902, 688835, 826506)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- audio-visual emotion recognition, depression detection, benchmark test set
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