The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.
Deep learning for robust feature generation in audiovisual emotion recognition
Abstract
Automatic emotion recognition systems predict high-level affective content from low-level human-centered signal cues. These systems have seen great improvements in classification accuracy, due in part to advances in feature selection methods. However, many of these feature selection methods capture only linear relationships between features or alternatively require the use of labeled data. In this paper we focus on deep learning techniques, which can overcome these limitations by explicitly capturing complex non-linear feature interactions in multimodal data. We propose and evaluate a suite of Deep Belief Network models, and demonstrate that these models show improvement in emotion classification performance over baselines that do not employ deep learning. This suggests that the learned high-order non-linear relationships are effective for emotion recognition.
Overview
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
“Kim et al. [7] developed a suite of deep belief net- work models that showed improvements in emotion clas- sification performance over baselines that do not use deep learning.”
From Multimodal emotion recognition using deep learning architectures (2016) · cites this paper as [7]
In the citing paper
“Kim et al. [159] and others (e.g., [149], [148]) demonstrated how this can be achieved with DBNs.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [159]
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,050
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1×
“The parameters of the RBM (W, b, c) are learned using contrastive divergence as in [33].”
MethodIEMOCAP: Interactive emotional dyadic motion capture database
Busso, C., Bulut, M., Lee, C.-C., Kazemzadeh, A., Mower, E., Kim, S., Chang, J. N., Lee, S., & Narayanan, S. S. (2008) · Language Resources and Evaluation · doi:10.1007/s10579-008-9076-6 · Cited by 3,869
(2011) · Cited by 2,752
Self-citationSurvey on speech emotion recognition: Features, classification schemes, and databases
Ayadi, M. M. H. E., Kamel, M. S., & Karray, F. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.09.020 · Cited by 2,139
Acoustic Modeling Using Deep Belief Networks
Mohamed, A., Dahl, G. E., & Hinton, G. E. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2109382 · Cited by 1,756
Information processing in dynamical systems: foundations of harmony theory
Smolensky, P. (1986) · The MIT Press eBooks · Cited by 1,673
Cited by
Sorted by citations
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
A comprehensive survey on support vector machine classification: Applications, challenges and trends
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.10.118 · Cited by 2,381
Speech Recognition Using Deep Neural Networks: A Systematic Review
Nassif, A. B., Shahin, I. M. A., Attili, I. B., Azzeh, M., & Shaalan, K. F. (2019) · IEEE Access · doi:10.1109/access.2019.2896880 · Cited by 1,215
Speech emotion recognition using deep 1D & 2D CNN LSTM networks
Zhao, J., Mao, X., & Chen, L. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.08.035 · Cited by 1,044
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.011 · Cited by 806
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
Speech emotion recognition with deep convolutional neural networks
Issa, D., Demirci, M. F., & Yazıcı, A. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101894 · Cited by 621
A Review of Human Activity Recognition Methods
Vrigkas, M., Nikou, C., & Kakadiaris, I. A. (2015) · Frontiers in Robotics and AI · doi:10.3389/frobt.2015.00028 · Cited by 571
Evaluating deep learning architectures for Speech Emotion Recognition
Fayek, H. M., Lech, M., & Cavedon, L. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.02.013 · Cited by 554
Schuller, B. W. (2018) · Communications of the ACM · doi:10.1145/3129340 · Cited by 547
Deep Multimodal Representation Learning: A Survey
Guo, W., Wang, J., & Wang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2916887 · Cited by 535
Emotion recognition using deep learning approach from audio–visual emotional big data
Hossain, M. S., & Muhammad, G. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.09.008 · Cited by 460
DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
Lane, N. D., Bhattacharya, S., Georgiev, P., Forlivesi, C., Jiao, L., Qendro, L., & Kawsar, F. (2016) · 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2016.7460664 · Cited by 450
Speech emotion recognition using convolutional and Recurrent Neural Networks
Lım, W., Jang, D., & Lee, T. (2016) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2016.7820699 · Cited by 433
Speech Emotion Recognition Using CNN
Huang, Z.-W., Dong, M., Mao, Q., & Zhan, Y. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654984 · Cited by 416
High-level feature representation using recurrent neural network for speech emotion recognition
Lee, J., & Tashev, I. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-336 · Cited by 408
Pacheco, F., Expósito, E., Gineste, M., Baudoin, C., & Aguilar, J. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2883147 · Cited by 387
Speech Emotion Recognition From 3D Log-Mel Spectrograms With Deep Learning Network
Meng, H., Yan, T., Yuan, F., & Wei, H. (2019) · IEEE Access · doi:10.1109/access.2019.2938007 · Cited by 356
Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition
Zhang, S., Zhang, S., Huang, T., Gao, W., & Tian, Q. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2719043 · Cited by 346
Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition
Li, J., Zhang, Z., & He, H. (2017) · Cognitive Computation · doi:10.1007/s12559-017-9533-x · Cited by 340
MMTM: Multimodal Transfer Module for CNN Fusion
Joze, H. R. V., Shaban, A., Iuzzolino, M. L., & Koishida, K. (2020) · 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/cvpr42600.2020.01330 · Cited by 337
Multimodal Deep Learning for Activity and Context Recognition
Radu, V., Tong, C., Bhattacharya, S., Lane, N. D., Mascolo, C., Marina, M. K., & Kawsar, F. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3161174 · Cited by 281
Emotion-aware connected healthcare big data towards 5G
Hossain, M. S., & Muhammad, G. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2772959 · Cited by 273
M3ER: Multiplicative Multimodal Emotion Recognition using Facial, Textual, and Speech Cues
Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i02.5492 · Cited by 23
Survey on Deep Neural Networks in Speech and Vision Systems
Alam, M., Samad, M. D., Vidyaratne, L., Glandon, A., & Iftekharuddin, K. M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.053 · Cited by 265
Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baselines
Poria, S., Majumder, N., Hazarika, D., Cambria, E., Gelbukh, A. F., & Hussain, A. (2018) · IEEE Intelligent Systems · doi:10.1109/mis.2018.2882362 · Cited by 264
Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243
Multimodal emotion recognition using deep learning architectures
Ranganathan, H., Chakraborty, S., & Panchanathan, S. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477679 · Cited by 249
Facial Expression Recognition in Video with Multiple Feature Fusion
Chen, J., Chen, Z., Chi, Z., & Fu, H. C. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2593719 · Cited by 225
Analyzing Personality through Social Media Profile Picture Choice
Leqi, L., Preoțiuc-Pietro, D., Samani, Z. R., Moghaddam, M. E., & Ungar, L. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14738 · Cited by 225
Scene Recognition by Manifold Regularized Deep Learning Architecture
Yuan, Y., Mou, L., & Lu, X. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2359471 · Cited by 214
Kim, Y., Soyata, T., & Feyzi-Behnagh, R. (2018) · IEEE Access · doi:10.1109/access.2018.2791861 · Cited by 230
Self-citationDeep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review
Samaras, S., Diamantidou, E., Ataloglou, D., Sakellariou, N., Vafeiadis, A., Magoulianitis, V., Lalas, A. X., Dimou, A., Zarpalas, D., Votis, K., et al. (2019) · Sensors · doi:10.3390/s19224837 · Cited by 226
Abdelhamid, A. A., El‐kenawy, E. M., Alotaibi, B., Amer, G. M., Abdelkader, M. Y., Ibrahim, A. F., & Eid, M. M. (2022) · IEEE Access · doi:10.1109/access.2022.3172954 · Cited by 194
Emotion recognition from unimodal to multimodal analysis: A review
Ezzameli, K., & Mahersia, H. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101847 · Cited by 206
Multimodal Transformer Fusion for Continuous Emotion Recognition
Huang, J., Tao, J., Liu, B., Lian, Z., & Niu, M. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053762 · Cited by 187
A Multi-Task Learning Framework for Emotion Recognition Using 2D Continuous Space
Xia, R., & Liu, Y. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2512598 · Cited by 181
Multimodal Measurement of Depression Using Deep Learning Models
Yang, L., Jiang, D., Xia, X., Pei, E., Oveneke, M. C., & Hichem Sahli, B. (2017) · doi:10.1145/3133944.3133948 · Cited by 175
Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition
Wen, G., Hou, Z., Li, H., Li, D., Jiang, L., & Xun, E. (2017) · Cognitive Computation · doi:10.1007/s12559-017-9472-6 · Cited by 164
A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data
Yu, L., Zhou, R., Tang, L., & Chen, R. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.04.049 · Cited by 163
3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms
Hajarolasvadi, N., & Demirel, H. (2019) · Entropy · doi:10.3390/e21050479 · Cited by 157
CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation
Etienne, C., Fidanza, G., Petrovskii, A., Devillers, L. Y., & Schmauch, B. (2018) · Workshop on Speech, Music and Mind (SMM) · doi:10.21437/smm.2018-5 · Cited by 161
A systematic literature review of speech emotion recognition approaches
Singh, Y. B., & Goel, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.028 · Cited by 156
Improved Multimodal Deep Learning with Variation of Information
Sohn, K., Shang, W., & Lee, H. (2014) · neural information processing systems · Cited by 145
Self-citationLearning deep features to recognise speech emotion using merged deep CNN
Zhao, J., Mao, X., & Chen, L. (2018) · IET Signal Processing · doi:10.1049/iet-spr.2017.0320 · Cited by 142
Audiovisual Fusion: Challenges and New Approaches
Katsaggelos, A. K., Bahaadini, S., & Molina, R. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2459017 · Cited by 143
Medical image analysis using wavelet transform and deep belief networks
Khatami, A., Khosravi, A., Nguyen, T. T., Lim, C. P., & Nahavandi, S. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.05.073 · Cited by 136
Nakisa, B., Rastgoo, M. N., Rakotonirainy, A., Maire, F., & Chandran, V. (2018) · IEEE Access · doi:10.1109/access.2018.2868361 · Cited by 141
Cai, Y., Li, X., & Li, J. (2023) · Sensors · doi:10.3390/s23052455 · Cited by 149
Gideon, J., McInnis, M. G., & Provost, E. M. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2916092 · Cited by 128
Self-citationLearning in high-dimensional multimedia data: the state of the art
Gao, L., Song, J., Liu, X., Shao, J., Liu, J., & Shao, J. (2015) · Multimedia Systems · doi:10.1007/s00530-015-0494-1 · Cited by 127
Representation Learning for Speech Emotion Recognition
Ghosh, S., Laksana, E., Morency, L., & Scherer, S. J. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-692 · Cited by 125
D-vlog: Multimodal Vlog Dataset for Depression Detection
Yoon, J., Kang, C., Kim, S., & Han, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21483 · Cited by 134
Cooperative Learning and its Application to Emotion Recognition from Speech
Zhang, Z., Coutinho, E., Deng, J., & Schuller, B. W. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2014.2375558 · Cited by 123
Alzheimer's disease and automatic speech analysis: A review
Pulido, M. L. B., Hernández, J. B. A., Ballester, M. Á. F., González, C. M. T., Mekyska, J., & Smékal, Z. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113213 · Cited by 120
The Human in Emotion Recognition on Social Media: Attitudes, Outcomes, Risks
Andalibi, N., & Buss, J. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376680 · Cited by 137
Emotion recognition from spontaneous speech using Hidden Markov models with deep belief networks
Le, D., & Provost, E. M. (2013) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2013.6707732 · Cited by 120
Self-citationIntegrating Deep and Shallow Models for Multi-Modal Depression Analysis—Hybrid Architectures
Yang, L., Jiang, D., & Hichem Sahli, B. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2870398 · Cited by 123
Machot, F. A., Elmachot, A., Ali, M., Machot, E. A., & Kyamakya, K. (2019) · Sensors · doi:10.3390/s19071659 · Cited by 121
Supervised Committee of Convolutional Neural Networks in Automated Facial Expression Analysis
Pons, G., & Masip, D. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2753235 · Cited by 110
A survey of multimodal deep generative models
Suzuki, M., & Matsuo, Y. (2022) · Advanced Robotics · doi:10.1080/01691864.2022.2035253 · Cited by 109
“[82] propose a multimodal generative model based on deep belief networks [28, 29], which has since been used in various multimodal applications [34, 41].”
BackgroundFeature learning based on SAE–PCA network for human gesture recognition in RGBD images
Li, S., Yu, B., Wu, W., Su, S., & Ji, R. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.06.086 · Cited by 108
Multimodal Deep Convolutional Neural Network for Audio-Visual Emotion Recognition
Zhang, S., Zhang, S. S., Huang, T., & Gao, W. (2016) · International Conference on Multimedia Retrieval · doi:10.1145/2911996.2912051 · Cited by 107
Deep spatio-temporal feature fusion with compact bilinear pooling for multimodal emotion recognition
Nguyen, D. H., Nguyen, K., Sridharan, S., Dean, D., & Fookes, C. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.06.005 · Cited by 108
Emotion Recognition in the Wild with Feature Fusion and Multiple Kernel Learning
Chen, J., Chen, Z., Chi, Z., & Fu, H. C. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2666277 · Cited by 104
Emotion recognition using secure edge and cloud computing
Hossain, M. S., & Muhammad, G. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.040 · Cited by 104
Towards Robust Discriminative Projections Learning via Non-Greedy -Norm MinMax
Nie, F., Wang, Z., Wang, R., Wang, Z., & Li, X. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2961877 · Cited by 104
Yan, J., Zheng, W., Xu, Q., Lu, G., Li, H., & Wang, B. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2557721 · Cited by 102
Automatic Emotion Recognition Using Temporal Multimodal Deep Learning
Nakisa, B., Rastgoo, M. N., Rakotonirainy, A., Maire, F., & Chandran, V. (2020) · IEEE Access · doi:10.1109/access.2020.3027026 · Cited by 101
Deep Neural Networks for Emotion Recognition Combining Audio and Transcripts
Cho, J., Pappagari, R. R., Kulkarni, P., Villalba, J., Carmiel, Y., & Dehak, N. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-2466 · Cited by 105
Yoo, Y., Tang, L. Y. W., Brosch, T., Li, D. K. B., Kolind, S., Vavasour, I. M., Rauscher, A., MacKay, A. L., Traboulsee, A. L., & Tam, R. C. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.10.015 · Cited by 103
Recognition of emotion in music based on deep convolutional neural network
Sarkar, R., Choudhury, S., Dutta, S., Roy, A., & Saha, S. K. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08192-x · Cited by 98
Multimodal Deep Learning Framework for Mental Disorder Recognition
Zhang, Z., Lin, W., Liu, M., & Mahmoud, M. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00033 · Cited by 89
A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM
Huang, C., Gong, W. Y., Fu, W., & Feng, D. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/749604 · Cited by 87
Subramaniam, A., Patel, V., Mishra, A. K., Balasubramanian, P., & Mittal, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_27 · Cited by 86
Deep Spatio-Temporal Features for Multimodal Emotion Recognition
Nguyen, D. H., Nguyen, K., Sridharan, S., Ghasemi, A., Dean, D., & Fookes, C. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.140 · Cited by 83
A LSTM based deep learning network for recognizing emotions using wireless brainwave driven system
Sakalle, A., Tomar, P., Bhardwaj, H., Acharya, D., & Bhardwaj, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114516 · Cited by 82
Zou, Z., Zhao, X., Zhao, P., Qi, F., & Wang, N. (2019) · Journal of Cultural Heritage · doi:10.1016/j.culher.2019.02.002 · Cited by 85
Campos, V., Salvador, A., Giró-i-Nieto, X., & Jou, B. (2015) · doi:10.1145/2813524.2813530 · Cited by 82
Multi-Modal Sequence Fusion via Recursive Attention for Emotion Recognition
Beard, R., Das, R., Ng, R. W. M., Gopalakrishnan, P. G. K., Eerens, L., Świętojański, P., & Mikšík, O. (2018) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k18-1025 · Cited by 77
Recognizing Induced Emotions of Movie Audiences from Multimodal Information
Muszyński, M., Tian, L., Lai, C., Moore, J. D., Κωστούλας, Θ., Lombardo, P., Pun, T., & Chanel, G. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2902091 · Cited by 79
Real-time speech emotion recognition using deep learning and data augmentation
Barhoumi, C., & BenAyed, Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-11065-x · Cited by 76
Using Synthetic Data to Improve Facial Expression Analysis with 3D Convolutional Networks
Abbasnejad, I., Sridharan, S., Nguyen, D., Denman, S., Fookes, C., & Lucey, S. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.189 · Cited by 73
Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey
Shishvan, O. R., Zois, D., & Soyata, T. (2018) · IEEE Access · doi:10.1109/access.2018.2866049 · Cited by 73
Touati, R., Mignotte, M., & Dahmane, M. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2964409 · Cited by 76
Learning Human Activities through Wi-Fi Channel State Information with Multiple Access Points
Li, H., Ota, K., Dong, M., & Guo, M. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700083 · Cited by 73
Modeling subjectiveness in emotion recognition with deep neural networks: Ensembles vs soft labels
Fayek, H. M., Lech, M., & Cavedon, L. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727250 · Cited by 73
Han, J., Zhang, Z., Cummins, N., & Schuller, B. W. (2019) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2019.2901088 · Cited by 86
Improved SMOTE Algorithm to Deal with Imbalanced Activity Classes in Smart Homes
Guo, S., Liu, Y., Chen, R., Sun, X. N., & Wang, X. (2018) · Neural Processing Letters · doi:10.1007/s11063-018-9940-3 · Cited by 71
Emotion Recognition by Fusing Time Synchronous and Time Asynchronous Representations
Wu, W., Zhang, C., & Woodland, P. C. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414880 · Cited by 70
Audio-visual emotion recognition using multi-directional regression and Ridgelet transform
Hossain, M. S., & Muhammad, G. (2015) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-015-0207-2 · Cited by 68
Data Augmentation for Audio-Visual Emotion Recognition with an Efficient Multimodal Conditional GAN
Ma, F., Li, Y., Ni, S., Huang, S., & Zhang, L. (2022) · Applied Sciences · doi:10.3390/app12010527 · Cited by 71
Qadri, S. F., Ai, D., Hu, G., Ahmad, M., Huang, Y., Wang, Y., & Yang, J. (2018) · Applied Sciences · doi:10.3390/app9010069 · Cited by 67
Deep Canonical Time Warping for Simultaneous Alignment and Representation Learning of Sequences
Trigeorgis, G., Nicolaou, M. A., Schuller, B. W., & Zafeiriou, S. P. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2710047 · Cited by 62
A deep-learning approach to facial expression recognition with candid images
Li, W., Li, M., Su, Z., & Zhu, Z. (2015) · Machine Vision Applications · doi:10.1109/mva.2015.7153185 · Cited by 62
A virtual environment for learning computer coding using gamification and emotion recognition
Cabada, R. Z., Estrada, M. L. B., Ríos-Félix, J. M., & Alor‐Hernández, G. (2018) · Interactive Learning Environments · doi:10.1080/10494820.2018.1558256 · Cited by 61
Guo, L., Wang, L., Dang, J., Liu, Z., & Guan, H. (2019) · IEEE Access · doi:10.1109/access.2019.2921390 · Cited by 62
Siddiqui, M. F. H., & Javaid, A. Y. (2020) · Multimodal Technologies and Interaction · doi:10.3390/mti4030046 · Cited by 63
Deep Learning based Emotion Recognition System Using Speech Features and TranscriptionsPreprint
Tripathi, S., Kumar, A., Ramesh, A., Singh, C., & Yenigalla, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05681 · Cited by 76
Metadata
- Authors
- Kim, Y., Lee, H., & Provost, E. M.
- Affiliations
- University of Michigan (US)
- Venue
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 3687-3691
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2009059481
- Cited by
- 401
- References
- 39
- Citations by year
- 2026: 5 · 2025: 19 · 2024: 22 · 2023: 18 · 2022: 33 · 2021: 36 · 2020: 44 · 2019: 50 · 2018: 49 · 2017: 47 · 2016: 29 · 2015: 30 · 2014: 17 · 2013: 2
- FWCI
- 35.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- 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
- audio-visual emotion recognition, deep belief network, feature selection, multimodal data, emotion classification, higher-order interactions
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
