OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit
Open accessAbstract
Various open-source toolkits exist for speech recognition and speech processing. These toolkits have brought a great benefit to the research community, i.e. speeding up research. Yet, no such freely available toolkit exists for automatic affect recognition from speech. We herein introduce a novel open-source affect and emotion recognition engine, which integrates all necessary components in one highly efficient software package. The components include audio recording and audio file reading, state-of-the-art paralinguistic feature extraction and plugable classification modules. In this paper we introduce the engine and extensive baseline results. Pre-trained models for four affect recognition tasks are included in the openEAR distribution. The engine is tailored for multi-threaded, incremental on-line processing of live input in real-time, however it can also be used for batch processing of databases.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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 current MER, it is common practice to use OpenSMILE, COVAREP, OpenEAR, and librosa to extract acoustic features that represent the emotional characteristics of the speech modality.
“In current MER, it is common practice to use OpenSMILE [45], COVAREP [46], OpenEAR [47], and librosa [48] to extract the aforementioned acoustic features that represent the emotional characteristics of the speech modality.”
From A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face (2023) · cites this paper as [47]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,124 · see citing works
Praat: Doing Phonetics by Computer
(2011) · Ear and Hearing · doi:10.1097/aud.0b013e31821473f7 · Cited by 8,004 · see citing works
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797 · see citing works
A database of German emotional speech
Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., & Weiss, B. (2005) · Interspeech 2005 · doi:10.21437/interspeech.2005-446 · Cited by 2,365 · see citing works
The INTERSPEECH 2009 emotion challenge
Schuller, B., Steidl, S., & Batliner, A. (2009) · Interspeech 2009 · doi:10.21437/interspeech.2009-103 · Cited by 938 · see citing works
Self-citationWöllmer, M., Eyben, F., Reiter, S. A., Schuller, B. W., Cox, C., Douglas‐Cowie, E., & Cowie, R. (2008) · Interspeech (USB) · doi:10.21437/interspeech.2008-192 · Cited by 329 · see citing works
Self-citationSchuller, B. W., Müller, R., Eyben, F., Gast, J., Hörnler, B., Wöllmer, M., Rigoll, G., Hoethker, A., & Konosu, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.02.013 · Cited by 194 · see citing works
Self-citationSchuller, B. W., Batliner, A. M., Seppi, D., Steidl, S., Vogt, T., Wagner, J., Devillers, L. Y., Vidrascu, L., Amir, N., Kessous, L., et al. (2007) · Interspeech (USB) · doi:10.21437/interspeech.2007-612 · Cited by 191 · see citing works
Self-citationEmoVoice — A Framework for Online Recognition of Emotions from Voice
Vogt, T., André, E., & Bee, N. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69369-7_21 · Cited by 156 · see citing works
Discriminative parameter learning for Bayesian networks
Su, J., Zhang, H., Ling, C. X., & Matwin, S. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390284 · Cited by 119 · see citing works
Support 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 · see citing works
Brute-forcing hierarchical functionals for paralinguistics: A waste of feature space?
Schuller, B. W., Wimmer, M., Mösenlechner, L., Kern, C., Arsić, D., & Rigoll, G. (2008) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2008.4518656 · Cited by 60 · see citing works
Self-citationSchuller, B. W., Eyben, F., & Rigoll, G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69369-7_12 · Cited by 52 · see citing works
Self-citationPsychological Motivated Multi-Stage Emotion Classification Exploiting Voice Quality Features
Lugger, M., & Yang, B. (2008) · InTech eBooks · doi:10.5772/6383 · Cited by 38 · see citing works
Flagged first, then by citations
1 of the 18 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [16][16] S. Young, G. Evermann, M. Gales, T. Hain, D. Kershaw, X. Liu, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev, and P. Woodland. The HTK book (v3.4). Cambridge University Press, Cambridge, UK, December 2006. -
Cited by
Sorted by citations
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 · see citing works
Self-citationA review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644 · see citing works
Speech emotion recognition using deep neural network and extreme learning machine
Han, K., Yu, D., & Tashev, I. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-57 · Cited by 824 · see citing works
Schuller, B. W., Batliner, A. M., Steidl, S., & Seppi, D. (2011) · Speech Communication · doi:10.1016/j.specom.2011.01.011 · Cited by 765 · see citing works
Self-citationCOVAREP — A collaborative voice analysis repository for speech technologies
Degottex, G., Kane, J., Drugman, T., Raitio, T., & Scherer, S. (2014) · 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2014.6853739 · Cited by 695 · see citing works
A systematic review on affective computing: emotion models, databases, and recent advances
Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.009 · Cited by 657 · see citing works
A survey of multimodal sentiment analysis
Soleymani, M., García, D., Jou, B., Schuller, B. W., Chang, S., & Pantić, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.08.003 · Cited by 620 · see citing works
Self-citationValstar, M., Schuller, B. W., Smith, K., Eyben, F., Jiang, B., Bilakhia, S., Schnieder, S., Cowie, R., & Pantić, M. (2013) · doi:10.1145/2512530.2512533 · Cited by 581 · see citing works
Self-citationThe INTERSPEECH 2010 paralinguistic challenge
Schuller, B. W., Steidl, S., Batliner, A. M., Burkhardt, F., Devillers, L. Y., Müller, C. A., & Narayanan, S. S. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-739 · Cited by 577 · see citing works
Self-citationMao, Q., Dong, M., Huang, Z., & Zhan, Y. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2360798 · Cited by 568 · see citing works
Fusing audio, visual and textual clues for sentiment analysis from multimodal content
Poria, S., Cambria, E., Howard, N., Huang, G.-B., & Hussain, A. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.01.095 · Cited by 534 · see citing works
3-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition
Chen, M., He, X., Yang, J., & Zhang, H. (2018) · IEEE Signal Processing Letters · doi:10.1109/lsp.2018.2860246 · Cited by 503 · see citing works
Valstar, M., Schuller, B. W., Smith, K., Almaev, T. R., Eyben, F., Krajewski, J., Cowie, R., & Pantić, M. (2014) · International Workshop on Audio/Visual Emotion Challenge (AVEC) · doi:10.1145/2661806.2661807 · Cited by 476 · see citing works
Self-citationTowards multimodal sentiment analysis: harvesting opinions from the web
Morency, L.-P., Mihalcea, R., & Doshi, P. (2011) · Proceedings of the 13th international conference on multimodal interfaces · doi:10.1145/2070481.2070509 · Cited by 470 · see citing works
Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies
Schuller, B. W., Vlasenko, B., Eyben, F., Wöllmer, M., Stuhlsatz, A., Wendemuth, A., & Rigoll, G. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.8 · Cited by 386 · see citing works
Self-citationRecurrent Neural Networks for Emotion Recognition in Video
Kahou, S. E., Michalski, V., Konda, K., Memisevic, R., & Pal, C. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2830596 · Cited by 386 · see citing works
Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition
Deng, J., Zhang, Z., Marchi, E., & Schuller, B. (2013) · 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction · doi:10.1109/acii.2013.90 · Cited by 366 · see citing works
Self-citationSpeech 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 · see citing works
Multimodal sentiment analysis based on fusion methods: A survey
Zhu, L., Zhu, Z., Zhang, C., Xu, Y., & Kong, X. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.02.028 · Cited by 368 · see citing works
Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015
Dhall, A., Ramana Murthy, O. V., Goecke, R., Joshi, J., & Gedeon, T. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2829994 · Cited by 305 · see citing works
Deep neural networks for acoustic emotion recognition: Raising the benchmarks
Stuhlsatz, A., Meyer, C., Eyben, F., Zielke, T., Meier, G., & Schuller, B. (2011) · 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2011.5947651 · Cited by 288 · see citing works
Self-citationParalinguistics in speech and language—State-of-the-art and the challenge
Schuller, B. W., Steidl, S., Batliner, A. M., Burkhardt, F., Devillers, L. Y., Müller, C. A., & Narayanan, S. S. (2012) · Computer Speech & Language · doi:10.1016/j.csl.2012.02.005 · Cited by 282 · see citing works
Self-citationCan Deep Learning Revolutionize Mobile Sensing?
Lane, N. D., & Georgiev, P. (2015) · Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications · doi:10.1145/2699343.2699349 · Cited by 282 · see citing works
Acoustic emotion recognition: A benchmark comparison of performances
Schuller, B. W., Vlasenko, B., Eyben, F., Rigoll, G., & Wendemuth, A. (2009) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2009.5372886 · Cited by 275 · see citing works
Self-citationAVEC 2011–The First International Audio/Visual Emotion Challenge
Schuller, B. W., Valstar, M., Eyben, F., McKeown, G. J., Cowie, R., & Pantić, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_53 · Cited by 270 · see citing works
Self-citationAutomated depression analysis using convolutional neural networks from speech
He, L., & Cui, C. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.05.007 · Cited by 250 · see citing works
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 · see citing works
(2013) · Cited by 236 · see citing works
Multimodal Sentiment Analysis of Spanish Online Videos
Rosas, V. P., Mihalcea, R. F., & Morency, L. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.9 · Cited by 228 · see citing works
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 · see citing works
Schuller, B. W., Valstar, M., Eyben, F., Cowie, R., & Pantić, M. (2012) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2388676.2388776 · Cited by 225 · see citing works
Self-citationThe INTERSPEECH 2011 speaker state challenge
Schuller, B. W., Steidl, S., Batliner, A. M., Schiel, F., & Krajewski, J. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-801 · Cited by 225 · see citing works
Self-citationEmotion Recognition In The Wild Challenge 2014
Dhall, A., Goecke, R., Joshi, J., Sikka, K., & Gedeon, T. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2666275 · Cited by 216 · see citing works
Wöllmer, M., Metallinou, A., Eyben, F., Schuller, B., & Narayanan, S. S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-646 · Cited by 201 · see citing works
Self-citationA Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face
Lian, H., Lu, C., Li, S., Zhao, Y., Tang, C., & Zong, Y. (2023) · Entropy · doi:10.3390/e25101440 · Cited by 224 · see citing works
“In current MER, it is common practice to use OpenSMILE [45], COVAREP [46], Open-EAR [47], and librosa [48] to extract the aforementioned acoustic features that represent the emotional characteristics of the speech modality.”
MethodDynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding
Dibeklioğlu, H., Hammal, Z., & Cohn, J. F. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2676878 · Cited by 196 · see citing works
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 · see citing works
Self-citationSurvey on audiovisual emotion recognition: databases, features, and data fusion strategies
Wu, C., Lin, J., & Wei, W.-L. (2014) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2014.11 · Cited by 185 · see citing works
Deep features-based speech emotion recognition for smart affective services
Badshah, A. M., Rahim, N., Ullah, N., Ahmad, J., Muhammad, K., Lee, M. Y., Kwon, S., & Baik, S. W. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5292-7 · Cited by 182 · see citing works
Automatic speaker age and gender recognition using acoustic and prosodic level information fusion
Li, M., Han, K. J., & Narayanan, S. S. (2012) · Computer Speech & Language · doi:10.1016/j.csl.2012.01.008 · Cited by 181 · see citing works
Online Driver Distraction Detection Using Long Short-Term Memory
Wöllmer, M., Blaschke, C., Schindl, T., Schuller, B. W., Färber, B., Mayer, S., & Trefflich, B. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2119483 · Cited by 177 · see citing works
Self-citationShoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102447 · Cited by 171 · see citing works
Multimodal Spatiotemporal Representation for Automatic Depression Level Detection
Niu, M., Tao, J., Liu, B., Huang, J., & Lian, Z. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3031345 · Cited by 171 · see citing works
Wöllmer, M., Schuller, B. W., Eyben, F., & Rigoll, G. (2010) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2010.2057200 · Cited by 164 · see citing works
Self-citationA survey of speech emotion recognition in natural environment
Fahad, M. S., Ranjan, A., Yadav, J., & Deepak, A. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102951 · Cited by 162 · see citing works
Emotion recognition in the wild challenge 2013
Dhall, A., Goecke, R., Joshi, J., Wagner, M., & Gedeon, T. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531739 · Cited by 178 · see citing works
Multimodal Video Sentiment Analysis Using Deep Learning Approaches, a Survey
Abdu, S. A., Yousef, A. H., & Salem, A. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.06.003 · Cited by 147 · see citing works
Survey on AI-Based Multimodal Methods for Emotion Detection
Marechal, C., Mikołajewski, D., Tyburek, K., Prokopowicz, P., Bougueroua, L., Ancourt, C., & Węgrzyn-Wolska, K. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-16272-6_11 · Cited by 137 · see citing works
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 · see citing works
Self-citationA study on the deployment of a service robot in an elderly care center
Portugal, D., Alvito, P., Christodoulou, E., Samaras, G., & Dias, J. (2019) · International Journal of Social Robotics · doi:10.1007/s12369-018-0492-5 · Cited by 130 · see citing works
Emotion Recognition From Speech With Recurrent Neural NetworksPreprint
Chernykh, V., & Prikhodko, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.08071 · Cited by 140 · see citing works
Unsupervised learning in cross-corpus acoustic emotion recognition
Zhang, Z., Weninger, F. J., Wöllmer, M., & Schuller, B. W. (2011) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2011.6163986 · Cited by 124 · see citing works
Self-citationYogeshC, K., Hariharan, M., Ngadiran, R., Adom, A., Yaacob, S., Berkai, C., & Polat, K. (2017) · Expert systems with applications · doi:10.1016/j.eswa.2016.10.035 · Cited by 115 · see citing works
Universal Onset Detection With Bidirectional Long Short-Term Memory Neural Networks.
Eyben, F., Böck, S., Schuller, B. W., & Graves, A. (2010) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · doi:10.5281/zenodo.1417130 · Cited by 119 · see citing works
Self-citationCHEAVD: a Chinese natural emotional audio–visual database
Li, Y., Tao, J., Chao, L., Bao, W., & Liu, Y. (2016) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-016-0406-z · Cited by 116 · see citing works
Deep learning approaches for speech emotion recognition: state of the art and research challenges
Jahangir, R., Teh, Y. W., Hanif, F., & Mujtaba, G. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09874-7 · Cited by 119 · see citing works
A Multimodal Emotion Detection System during Human–Robot Interaction
Alonso-Martín, F., Malfáz, M., Sequeira, J. S., Gorostiza, J. F., & Salichs, M. A. (2013) · Sensors · doi:10.3390/s131115549 · Cited by 117 · see citing works
On Acoustic Emotion Recognition: Compensating for Covariate Shift
Hassan, A., Damper, R. I., & Niranjan, M. (2013) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2013.2255278 · Cited by 112 · see citing works
Wang, Z.-Q., & Tashev, I. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953138 · Cited by 107 · see citing works
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 · see citing works
The SEMAINE API: Towards a Standards-Based Framework for Building Emotion-Oriented Systems
Schröder, M. (2010) · Advances in Human-Computer Interaction · doi:10.1155/2010/319406 · Cited by 104 · see citing works
Allison, T. H., Warnick, B. J., Davis, B. C., & Cardon, M. S. (2022) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2022.106193 · Cited by 95 · see citing works
Maxhuni, A., Muñoz-Meléndez, A., Osmani, V., Perez, H., Mayora, O., & Morales, E. F. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.01.008 · Cited by 93 · see citing works
A Survey of Cutting-edge Multimodal Sentiment Analysis
Singh, U., Abhishek, K., & Azad, H. K. (2024) · ACM Computing Surveys · doi:10.1145/3652149 · Cited by 99 · see citing works
A supervised approach to movie emotion tracking
Malandrakis, N., Potamianos, A., Evangelopoulos, G., & Zlatintsi, A. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946961 · Cited by 93 · see citing works
Detecting emotional state of a child in a conversational computer game
Yıldırım, S., Narayanan, S. S., & Potamianos, A. (2010) · Computer Speech & Language · doi:10.1016/j.csl.2009.12.004 · Cited by 91 · see citing works
A social emotion classification approach using multi-model fusion
Xu, G., Li, W., & Liu, J. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.07.007 · Cited by 88 · see citing works
Towards Automatic Detection of Misinformation in Online Medical Videos
Hou, R., Pérez‐Rosas, V., Loeb, S., & Mihalcea, R. F. (2019) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3340555.3353763 · Cited by 86 · see citing works
Understanding and Predicting Empathic Behavior in Counseling Therapy
Pérez‐Rosas, V., Mihalcea, R. F., Resnicow, K., Singh, S. P., & An, L. C. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1131 · Cited by 82 · see citing works
Towards Efficient Multi-Modal Emotion Recognition
Dobrišek, S., Gajšek, R., Mihelič, F., Pavešić, N., & Štruc, V. (2013) · International Journal of Advanced Robotic Systems · doi:10.5772/54002 · Cited by 77 · see citing works
Perceptual audio features for emotion detection
Sezgin, M. C., Günsel, B., & Kurt, G. K. (2012) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/1687-4722-2012-16 · Cited by 78 · see citing works
Using multiple databases for training in emotion recognition: to unite or to vote?
Schuller, B. W., Zhang, Z., Weninger, F. J., & Rigoll, G. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-468 · Cited by 76 · see citing works
Self-citationThe Automatic Recognition of Emotions in Speech
Batliner, A. M., Schuller, B. W., Seppi, D., Steidl, S., Devillers, L. Y., Vidrascu, L., Vogt, T., Aharonson, V., & Amir, N. (2010) · Cognitive technologies · doi:10.1007/978-3-642-15184-2_6 · Cited by 75 · see citing works
Self-citationLive Speech Driven Head-and-Eye Motion Generators
Le, B. H., Ma, X., & Deng, Z. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.74 · Cited by 74 · see citing works
Deng, J., Xia, R., Zhang, Z., Liu, Y., & Schuller, B. W. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854517 · Cited by 72 · see citing works
Self-citationAnalysis of Emotional Speech—A Review
Gangamohan, P., Kadiri, S. R., & Yegnanarayana, B. (2016) · Intelligent systems reference library · doi:10.1007/978-3-319-31056-5_11 · Cited by 72 · see citing works
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future TrendsPreprint
Latif, S., Rana, R. K., Khalifa, S., Jurdak, R., Qadir, J., & Schuller, B. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.00378 · Cited by 87 · see citing works
Self-citationAfzal, S., Khan, H. A., Piran, M. J., & Lee, J. W. (2024) · IEEE Access · doi:10.1109/access.2024.3422480 · Cited by 75 · see citing works
Harnessing A.I. for Augmenting Creativity
Smith, J. R., Joshi, D., Huet, B., Hsu, W. H., & Cota, J. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3127906 · Cited by 67 · see citing works
Speech Emotion Recognition Using Clustering Based GA-Optimized Feature Set
Kanwal, S., & Asghar, S. (2021) · IEEE Access · doi:10.1109/access.2021.3111659 · Cited by 64 · see citing works
Shape-based modeling of the fundamental frequency contour for emotion detection in speech
Arias, J. P., Busso, C., & Yoma, N. B. (2013) · Computer Speech & Language · doi:10.1016/j.csl.2013.07.002 · Cited by 62 · see citing works
Guo, L., Wang, L., Dang, J., Liu, Z., & Guan, H. (2019) · IEEE Access · doi:10.1109/access.2019.2921390 · Cited by 61 · see citing works
Modeling hidden dynamics of multimodal cues for spontaneous agreement and disagreement recognition
Bousmalis, K., Morency, L., & Pantić, M. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771341 · Cited by 58 · see citing works
Unsupervised domain adaptation for speech emotion recognition using PCANet
Huang, Z.-W., Xue, W., Mao, Q., & Zhan, Y. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3354-x · Cited by 59 · see citing works
Dynamic Emotion Modeling With Learnable Graphs and Graph Inception Network
Shirian, A., Tripathi, S., & Guha, T. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3059169 · Cited by 58 · see citing works
Pfister, T., & Robinson, P. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.8 · Cited by 60 · see citing works
Cross-Corpus Classification of Realistic Emotions - Some Pilot Experiments
Eyben, F., Batliner, A. M., Schuller, B. W., Seppi, D., & Steidl, S. (2010) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 55 · see citing works
Self-citationA Feature Fusion Method Based on Extreme Learning Machine for Speech Emotion Recognition
Guo, L., Wang, L., Dang, J., Zhang, L., & Guan, H. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462219 · Cited by 56 · see citing works
Multi-class and hierarchical SVMs for emotion recognition
Hassan, A., & Damper, R. I. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-644 · Cited by 55 · see citing works
Schuller, B. W., Weninger, F. J., Zhang, Y., Ringeval, F., Batliner, A. M., Steidl, S., Eyben, F., Marchi, E., Vinciarelli, A., Scherer, K. R., et al. (2018) · Computer Speech & Language · doi:10.1016/j.csl.2018.02.004 · Cited by 56 · see citing works
Self-citationA Proposed Feature Extraction Method for EEG-based Person Identification
Nguyen, T. P., Tran, D., Huang, X., & Sharma, D. K. (2012) · International Conference on Artificial Intelligence · Cited by 53 · see citing works
Machine learning- and statistical-based voice analysis of Parkinson’s disease patients: A survey
Amato, F., Saggio, G., Cesarini, V., Olmo, G., & Costantini, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119651 · Cited by 52 · see citing works
Medium-term speaker states—A review on intoxication, sleepiness and the first challenge
Schuller, B. W., Steidl, S., Batliner, A. M., Schiel, F., Krajewski, J., Weninger, F. J., & Eyben, F. (2012) · Computer Speech & Language · doi:10.1016/j.csl.2012.12.002 · Cited by 52 · see citing works
Self-citationSpeech Emotion Recognition via an Attentive Time–Frequency Neural Network
Lu, C., Zheng, W., Lian, H., Zong, Y., Tang, C., Li, S., & Zhao, Y. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3219825 · Cited by 51 · see citing works
“For instance, Schuller et al. [11] integrated 6552 hand-crafted features into an open-source toolkit, i.e., open-EAR, for four affective recognition tasks and then extended the openEAR to a multifunctional and convenient toolkit for diverse speech-related tasks, i.e., openSMILE [12].”
BackgroundMethodReal-life emotion-related states detection in call centers: a cross-corpora study
Devillers, L. Y., Vaudable, C., & Chastagnol, C. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-643 · Cited by 52 · see citing works
Deep Learning Models for Speech Emotion Recognition
Praseetha, V. M., & Vadivel, S. (2018) · Journal of Computer Science · doi:10.3844/jcssp.2018.1577.1587 · Cited by 49 · see citing works
Automatic classification of married couples' behavior using audio features
Black, M., Katsamanis, A., Lee, C.-C., Lammert, A. C., Baucom, B., Christensen, A., Georgiou, P., & Narayanan, S. S. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-574 · Cited by 50 · see citing works
YogeshC, K., Hariharan, M., Ngadiran, R., Adom, A., Yaacob, S., & Polat, K. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.03.013 · Cited by 48 · see citing works
Mao, Q., Xu, G., Xue, W., Gou, J., & Zhan, Y. (2017) · Speech Communication · doi:10.1016/j.specom.2017.06.006 · Cited by 48 · see citing works
Voice attributes affecting likability perception
Weiß, B., & Burkhardt, F. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-570 · Cited by 46 · see citing works
- Authors
- Eyben, F., Wollmer, M., & Schuller, B.
- Affiliations
- Technical University of Munich (DE)
- ORCID
- Eyben, Florian
- Venue
- 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 1-6
- ISSN
- 2156-8103 (ISSN-L)
- OpenAlex
- W2167854178
- Cited by
- 418
- References
- 18
- Citations by year
- 2026: 2 · 2025: 12 · 2024: 20 · 2023: 21 · 2022: 20 · 2021: 14 · 2020: 21 · 2019: 30 · 2018: 18 · 2017: 31 · 2016: 24 · 2015: 35 · 2014: 25 · 2013: 36 · 2012: 27
- FWCI
- 62.94
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Affective Computing and Intelligent Interaction and workshops (publishedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- affect recognition, emotion recognition, speech emotion recognition, paralinguistic features, open-source software toolkit, real-time speech processing, audio recordings, affective computing
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
