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
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-citationToward machine emotional intelligence: analysis of affective physiological state
Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334
Intransitivity of preferences.
Tversky, A. (1969) · Psychological Review · doi:10.1037/h0026750 · Cited by 2,193
Cacioppo, J. T., Tassinary, L. G., Berntson, G. G., Shan, H., Mason, P., Kolb, B., Whishaw, I. Q., Geuter, S., Lindquist, M. A., Wager, T. D., et al. (2016) · Cambridge University Press eBooks · doi:10.1017/9781107415782 · Cited by 2,032
Supervised Descent Method and Its Applications to Face Alignment
Xiong, X., & la Torre, F. D. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.75 · Cited by 1,926
Dawson, M. E., Schell, A. M., Filion, D. L., & Berntson, G. G. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546396.007 · Cited by 1,776
Gradient Flow in Recurrent Nets: The Difficulty of Learning LongTerm Dependencies
(2009) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470544037.ch14 · Cited by 1,312
Recent developments in openSMILE, the munich open-source multimedia feature extractor
Eyben, F., Weninger, F. J., Gross, F., & Schuller, B. W. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502224 · Cited by 1,265
Self-citationEmotional speech recognition: Resources, features, and methods
Ververidis, D., & Kotropoulos, C. L. (2006) · Speech Communication · doi:10.1016/j.specom.2006.04.003 · Cited by 930
Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies
Hochreiter, S., Bengio, Y., Informatik, F. F., Bengio, Y., Frasconi, P., & Schmidhuber, J. (2001) · Cited by 863
Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions
Ringeval, F., Sonderegger, A., Sauer, J., & Lalanne, D. (2013) · 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2013.6553805 · Cited by 771
Self-citationSchuller, B. W., Steidl, S., Batliner, A. M., Vinciarelli, A., Scherer, K. R., Ringeval, F., Chétouani, M., Weninger, F. J., Eyben, F., Marchi, E., et al. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-56 · Cited by 732
Self-citationDynamic markers of altered gait rhythm in amyotrophic lateral sclerosis
Hausdorff, J. M., Lertratanakul, A., Cudkowicz, M., Peterson, A. L., Kaliton, D., & Goldberger, A. L. (2000) · Journal of Applied Physiology · doi:10.1152/jappl.2000.88.6.2045 · Cited by 502
Nicolaou, M. A., Gunes, H., & Pantic, M. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.9 · Cited by 450
How Fast are the Leaked Facial Expressions: The Duration of Micro-Expressions
Yan, W., Wu, Q., Liang, J., Chen, Y.-H., & Fu, X. (2013) · Journal of Nonverbal Behavior · doi:10.1007/s10919-013-0159-8 · Cited by 446
Automatic, Dimensional and Continuous Emotion Recognition
Güneş, H., & Pantić, M. (2010) · International Journal of Synthetic Emotions · doi:10.4018/jse.2010101605 · Cited by 443
The Vera am Mittag German audio-visual emotional speech database
Grimm, M., Kroschel, K., & Narayanan, S. S. (2008) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2008.4607572 · Cited by 413
Gunes, H., & Schuller, B. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.06.016 · Cited by 360
Self-citationA Field Guide to Dynamical Recurrent Networks
Kolen, J. F., & Kremer, S. C. (2009) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470544037 · Cited by 354
Douglas‐Cowie, E., Cowie, R., Sneddon, I., Cox, C., Lowry, O. M., McRorie, M., Martin, J., Devillers, L. Y., Abrilian, S., Batliner, A. M., et al. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74889-2_43 · Cited by 323
openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor
Eyben, F. ;. (2013) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 322
Self-citationThe Effects of a Normative Intervention on Group Decision-Making Performance
Hall, J., & Watson, W. (1970) · Human Relations · doi:10.1177/001872677002300404 · Cited by 313
On the Acoustics of Emotion in Audio: What Speech, Music, and Sound have in Common
Weninger, F. J., Eyben, F., Schuller, B. W., Mortillaro, M., & Scherer, K. R. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00292 · Cited by 277
Self-citationSocial psychophysiology and emotion : theory and clinical applications
Wagner, H. L. (1988) · Cited by 241
Emotion and the autonomic nervous system: A prospectus for research on autonomic specificity.
LEVENSON, R. W. (1988) · Cited by 236
Bilchick, K. C., & Berger, R. D. (2006) · Journal of Cardiovascular Electrophysiology · doi:10.1111/j.1540-8167.2006.00501.x · Cited by 193
Building autonomous sensitive artificial listeners
Schroder, M., Bevacqua, E., Cowie, R., Eyben, F., Gunes, H., Heylen, D., ter Maat, M., McKeown, G., Pammi, S., Pantic, M., et al. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.34 · Cited by 185
Self-citationRobust full‐motion recovery of head by dynamic templates and re‐registration techniques
Xiao, J., Moriyama, T., Takeo Kanade, A., & Cohn, J. F. (2003) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.10048 · Cited by 184
Real-time Speech and Music Classification by Large Audio Feature Space Extraction
Eyben, F. (2015) · Springer theses · doi:10.1007/978-3-319-27299-3 · Cited by 161
Self-citationBimodal Emotion Recognition using Speech and Physiological Changes
Kim, J. (2007) · doi:10.5772/4754 · Cited by 146
Eyben, F., Wöllmer, M., Graves, A., Schuller, B. W., Douglas‐Cowie, E., & Cowie, R. (2009) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-009-0032-6 · Cited by 137
Self-citationThe INTERSPEECH 2014 computational paralinguistics challenge: cognitive & physical load
Schuller, B. W., Steidl, S., Batliner, A. M., Epps, J., Eyben, F., Ringeval, F., Marchi, E., & Zhang, Y. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-104 · Cited by 120
Self-citationSupport Vector Regression for Automatic Recognition of Spontaneous Emotions in Speech
Grimm, M., Kroschel, K., & Narayanan, S. S. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.367262 · Cited by 114
Robust continuous prediction of human emotions using multiscale dynamic cues
Nicolle, J., Rapp, V., Bailly, K., Prévost, L., & Chétouani, M. (2012) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2388676.2388783 · Cited by 113
Correcting Time-Continuous Emotional Labels by Modeling the Reaction Lag of Evaluators
Mariooryad, S., & Busso, C. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2334294 · Cited by 111
Robust Speech Recognition and Understanding
Grimm, M., & Kroschel, K. (2007) · doi:10.5772/35 · Cited by 98
Frame vs. Turn-Level: Emotion Recognition from Speech Considering Static and Dynamic Processing
Vlasenko, B., Schuller, B. W., Wendemuth, A., & Rigoll, G. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74889-2_13 · Cited by 92
Self-citationAudio-Visual Classification and Fusion of Spontaneous Affective Data in Likelihood Space
Nicolaou, M. A., Güneş, H., & Pantić, M. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.900 · Cited by 74
Audio-visual emotion recognition using an emotion space concept
Kanluan, I., Grimm, M., & Kroschel, K. (2008) · Cited by 66
Analysis and Compensation of the Reaction Lag of Evaluators in Continuous Emotional Annotations
Mariooryad, S., & Busso, C. (2013) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2013.21 · Cited by 58
A multitask approach to continuous five-dimensional affect sensing in natural speech
Eyben, F., Wöllmer, M., & Schuller, B. W. (2012) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2133366.2133372 · Cited by 55
Self-citationTracking changes in continuous emotion states using body language and prosodic cues
Metallinou, A., Katsamanis, A., Wang, Y., & Narayanan, S. S. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946939 · Cited by 42
The INTERSPEECH 2014 Computational paralinguistics challenge: cognitive & physical load
Schuller, B., Steidl, S., Batliner, A., Epps, J., Eyben, F., Ringeval, F., Marchi, E., & Zhang, Y. (2019) · OPUS (Augsburg University) · Cited by 36
Self-citationAutomatic Segmentation of Spontaneous Data using Dimensional Labels from Multiple Coders
Nicolaou, M. A., Güneş, H., & Pantić, M. (2010) · University of Twente Research Information · Cited by 32
Extracting complexity waveforms from one-dimensional signals
Kalauzi, A. J., Bojić, T., & Rakić, L. (2009) · Nonlinear Biomedical Physics · doi:10.1186/1753-4631-3-8 · Cited by 27
Time-Scale Feature Extractions for Emotional Speech Characterization
Chétouani, M., Mahdhaoui, A., & Ringeval, F. (2009) · Cognitive Computation · doi:10.1007/s12559-009-9016-9 · Cited by 26
Self-citation2D Face Alignment and Pose Estimation Based on 3D Facial Models
Chen, S.-C., Wu, C., Lin, S.-Y., & Hung, Y. (2012) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2012.60 · Cited by 10
The Intrinsic Recurrent Support Vector Machine
Schneegaß, D., Schäfer, A. M., & Martinetz, T. (2007) · Cited by 5
Flagged first, then by citations
5 of the 63 references couldn’t be matched to a known paper.
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
