The DOI matches its Crossref record; 58 of the 63 references it cites were found in a registry or an index.
Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Learning joint feature representations for multimodal data. In multimodal approaches, DNNs are leveraged to learn joint feature representations from multiple unimodal feature representations to accomplish feature-level fusion [65].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [65]
In the citing paper
“They found that while the given physiological signal has lower predictive power than audiovisual signals, both are complementary.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [65]
In the citing paper
“One study suggested that a smaller window size (2s) could be the best choice [65].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [65]
In the citing paper
“Depending on the nature of the source audio, it can be beneficial to apply HSFs to frame-level LLDs according to a sliding window before applying the RNN [151], [65].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [65]
In the citing paper
“They concluded that in featurelevel fusion, ECG data helps for prediction of valence, but not arousal.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [65]
In the citing paper
“To date, the literature generally reports that decision-level fusion works better for affect recognition given the datasets and models currently used [65].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [65]
In the citing paper
“However, several other studies based on handcrafted features found that decision-level fusion on top of individual LSTM models leads to better performance [65], [129].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [65]
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785
A Real-Time QRS Detection Algorithm
Pan, J., & Tompkins, W. J. (1985) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1985.325532 · Cited by 7,855
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 6,152
Backpropagation through time: what it does and how to do it
Werbos, P. J. (1990) · Proceedings of the IEEE · doi:10.1109/5.58337 · Cited by 4,879
(2001) · doi:10.1017/cbo9780511546396 · Cited by 4,482
Supervised Sequence Labelling with Recurrent Neural Networks
Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120
Two-Frame Motion Estimation Based on Polynomial Expansion
Farnebäck, G. (2003) · Lecture notes in computer science · doi:10.1007/3-540-45103-x_50 · Cited by 3,112
Cited by
Sorted by citations
A review on the long short-term memory model
Houdt, G. V., Mosquera, C., & Nápoles, G. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09838-1 · Cited by 2,198
“Manyotherapplicationsexistfortimeseriespredictionscenarios, like emotion ratings (Ringeval et al. 2015), topic evolution in text streams (Lu et al. 2019), building energy usage (Wang et al. 2019b), carbon emission levels (Huang et al. 2019b), flight delays (McCarthy et al. 2019), financial trading…”
BackgroundAdieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network
Trigeorgis, G., Ringeval, F., Brueckner, R., Marchi, E., Nicolaou, M. A., Schuller, B., & Zafeiriou, S. (2016) · 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2016.7472669 · Cited by 904
“As a second baseline algorithm, we utilised a BLSTM-DRNNs with the architecture preserved from [5, 21], i.”
MethodInfluentialSelf-citationEnd-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
Self-citationValstar, M., Gratch, J. S., Schuller, B. W., Ringeval, F., Lalanne, D., Torres, M. T., Scherer, S. J., Stratou, G., Cowie, R., & Pantić, M. (2016) · International Workshop on Audio/Visual Emotion Challenge (AVEC) · doi:10.1145/2988257.2988258 · Cited by 540
“A normalisation technique based on the Evaluator Weighted Estimator [30], is used prior to the computation of the gold-standard, i. e., the average of all ratings for each subject [36].”
BackgroundMethodInfluentialSelf-citationFacial emotion recognition using deep learning: review and insights
Mellouk, W., & Handouzi, W. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.07.101 · Cited by 370
Metadata
- Authors
- Ringeval, F., Eyben, F., Kroupi, E., Yuce, A., Thiran, J.-P., Ebrahimi, T., Lalanne, D., & Schuller, B.
- Affiliations
- University of Fribourg (CH)
- Technical University of Munich (DE)
- École Polytechnique Fédérale de Lausanne (CH)
- University of Passau (DE)
- Corresponding author
- Ringeval, Fabien
- ORCID
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2015-11
- Volume
- 66
- Pages
- 22-30
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2005418748
- Cited by
- 202
- References
- 63
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 4 · 2023: 15 · 2022: 14 · 2021: 14 · 2020: 17 · 2019: 25 · 2018: 38 · 2017: 23 · 2016: 30 · 2015: 9
- FWCI
- 26.12
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- Pattern Recognition Letters (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- OPUS (Augsburg University) (submittedVersion, cc-by-nc-nd)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Seventh Framework Programme
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- automatic emotion recognition, arousal, valence, long short-term memory, recurrent neural networks, multimodal fusion, feature-level fusion, decision fusion, audiovisual information, physiological signals, ECG, electrodermal activity
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
