Strength modelling for real-worldautomatic continuous affect recognition from audiovisual signals
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Only two other models in the literature use both speech and visual modalities on the RECOLA database: the Output-Associative Relevance Vector Machine Staircase Regression (OA RVM-SR) and the strength modeling system proposed by Han et al.
“Only two other models found in the literature to use both speech and visual modalities on the RECOLA database. These are the Output-Associative Relevance Vector Machine Staircase Regression (OA RVM-SR) [30] and the strength modeling system proposed by Han et al. [28].”
From End-to-End Multimodal Emotion Recognition using Deep Neural Networks (2017) · cites this paper as [28]
Cites
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences
Cohen (2013) · doi:10.4324/9780203774441 · Cited by 3,228 · see citing works
McKeown, G., Valstar, M., Cowie, R., Pantic, M., & Schroder, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.20 · Cited by 665 · see citing works
LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Wöllmer, M., Kaiser, M., Eyben, F., Schuller, B., & Rigoll, G. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.03.001 · Cited by 288 · see citing works
Self-citationPrediction of asynchronous dimensional emotion ratings from audiovisual and physiological data
Ringeval, F., Eyben, F., Kroupi, E., Yuce, A., Thiran, J.-P., Ebrahimi, T., Lalanne, D., & Schuller, B. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.11.007 · Cited by 202 · cited 4× · see citing works
“This finding is in agreement with similar results in the literature [8, 10, 26].”
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End-to-End Multimodal Emotion Recognition using Deep Neural Networks
Tzirakis, P., Trigeorgis, G., Nicolaou, M. A., Schuller, B., & Zafeiriou, S. (2017) · arXiv · doi:10.48550/arxiv.1704.08619 · Cited by 752 · see citing works
Self-citationSewa db: A rich database for audio-visual emotion and sentiment research in the wild
Kossaifi, J., Walecki, R., Panagakis, Y., Shen, J., Schmitt, M., Ringeval, F., Han, J., Pandit, V., Toisoul, A., Schuller, B., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2944808 · Cited by 243 · see citing works
Self-citation
- Authors
- Han, J., Zhang, Z., Cummins, N., Ringeval, F., & Schuller, B.
- Affiliations
- University of Passau (DE)
- Corresponding author
- Zhang, Zixing
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 65
- Pages
- 76-86
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W2560025328
- Cited by
- 53
- References
- 4
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 7 · 2022: 4 · 2021: 7 · 2020: 9 · 2019: 8 · 2018: 9 · 2017: 5
- FWCI
- 9.25
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Licence
- public-domain
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Image and Vision Computing (publishedVersion)
- HAL (submittedVersion, public-domain)
- OPUS (Augsburg University) (submittedVersion, cc-by-nc-nd)
- Image and Vision Computing
- Funders
- Horizon 2020 (645094)
- EC's Seventh Framework Programme (338164)
- Nvidia
- 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
- support vector regression, audio-visual emotion recognition, spontaneous emotions, feature-level fusion, decision fusion
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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.
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