The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
The INTERSPEECH 2009 emotion challenge
Abstract
The last decade has seen a substantial body of literature on the recognition of emotion from speech. However, in comparison to related speech processing tasks such as Automatic Speech and Speaker Recognition, practically no standardised corpora and test-conditions exist to compare performances under exactly the same conditions. Instead a multiplicity of evaluation strategies employed – such as cross-validation or percentage splits without proper instance definition – prevents exact reproducibility. Further, in order to face more realistic scenarios, the community is in desperate need of more spontaneous and less prototypical data. This INTERSPEECH 2009 Emotion Challenge aims at bridging such gaps between excellent research on human emotion recognition from speech and low compatibility of results. The FAU Aibo Emotion Corpus [1] serves as basis with clearly defined test and training partitions incorporating speaker independence and different room acoustics as needed in most reallife settings. This paper introduces the challenge, the corpus, the features, and benchmark results of two popular approaches towards emotion recognition from speech. Index Terms: emotion, challenge, feature types, classification 1.
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
“On the FAU Aibo Emotion Corpus [30], as part of the INTERSPEECH Emotion Challenge 2009 [25], we were able to slightly exceed the baseline given by the organizers for a five class problem (anger, emphatic, neutral, positive, and rest) [32].”
From Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data (2011) · cites this paper as [25]
In the citing paper
“This number varies from several hundred [157] to several thousand [158], [123], depending on the employed LLDs and HSFs.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [157]
Citations
Cites
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
Emotion recognition in human-computer interaction
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001) · IEEE Signal Processing Magazine · doi:10.1109/79.911197 · Cited by 2,603
Speech recognition by machines and humans
Lippmann, R. P. (1997) · Speech Communication · doi:10.1016/s0167-6393(97)00021-6 · Cited by 531
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
Schuller, 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
Self-citationCombining Efforts for Improving Automatic Classification of Emotional User States
Batliner, A., Steidl, S., Schuller, B., Seppi, D., Laskowski, K., Vogt, T., Devillers, L., Vidrascu, L., Amir, N., Kessous, L., et al. (2006) · Jožef Stefan Institute, Ljubljana · doi:10.5281/zenodo.14167504 · Cited by 119
Self-citationA State of the Art Review on Emotional Speech Databases
Ververidis, D., & Kotropoulos, K. (2003) · Cited by 88
Audiovisual recognition of spontaneous interest within conversations
Schuller, B. W., Müeller, R., Hörnler, B., Hoethker, A., Konosu, H., & Rigoll, G. (2007) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1322192.1322201 · Cited by 85
Self-citationPatterns, prototypes, performance: classifying emotional user states
Seppi, D., Batliner, A. M., Schuller, B. W., Steidl, S., Vogt, T., Wagner, J., Devillers, L. Y., Vidrascu, L., Amir, N., & Aharonson, V. (2008) · Interspeech (USB) · doi:10.21437/interspeech.2008-193 · Cited by 41
Self-citation
Flagged first, then by citations
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
Self-citationThe Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing
Eyben, F., Scherer, K. R., Schuller, B. W., Sundberg, J., Andre, E., Busso, C., Devillers, L. Y., Epps, J., Laukka, P., Narayanan, S. S., et al. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2457417 · Cited by 1,818
Self-citationReview on Psychological Stress Detection Using Biosignals
Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., & Tsiknakis, M. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2927337 · Cited by 764
Schuller, B. W., Batliner, A. M., Steidl, S., & Seppi, D. (2011) · Speech Communication · doi:10.1016/j.specom.2011.01.011 · Cited by 765
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
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
Self-citationEvaluating 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
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
Self-citationFeatures and classifiers for emotion recognition from speech: a survey from 2000 to 2011
Anagnostopoulos, C.-N., Iliou, T., & Giannoukos, I. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-012-9368-5 · Cited by 428
OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit
Eyben, F., Wollmer, M., & Schuller, B. (2009) · 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops · doi:10.1109/acii.2009.5349350 · Cited by 418
Self-citationDeep learning for robust feature generation in audiovisual emotion recognition
Kim, Y., Lee, H., & Provost, E. M. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638346 · Cited by 401
MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception
Busso, C., Parthasarathy, S., Burmania, A., AbdelWahab, M., Sadoughi, N., & Provost, E. M. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2515617 · Cited by 391
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
Self-citationMachine Recognition of Music Emotion
Yang, Y., & Chen, H. H. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2168752.2168754 · Cited by 405
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
Self-citationGunes, H., & Schuller, B. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.06.016 · Cited by 360
Self-citationEmotion recognition using a hierarchical binary decision tree approach
Lee, C.-C., Mower, E., Busso, C., Lee, S., & Narayanan, S. S. (2011) · Speech Communication · doi:10.1016/j.specom.2011.06.004 · Cited by 366
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
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 296
Self-citationSpeech Emotion Classification Using Attention-Based LSTM
Xie, Y., Liang, R., Liang, Z., Huang, C., Zou, C., & Schuller, B. W. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2925934 · Cited by 286
Self-citationOn 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-citationAcoustic 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
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
Self-citationAutoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
Deng, J., Zhang, Z., Eyben, F., & Schuller, B. W. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2324759 · Cited by 268
Self-citationWu, C., & Liang, W.-B. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.16 · Cited by 269
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
Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises
Bone, D., Goodwin, M. S., Black, M. P., Lee, C.-C., Audhkhasi, K., & Narayanan, S. S. (2014) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-014-2268-6 · Cited by 239
A Review on Speech Emotion Recognition Using Deep Learning and Attention Mechanism
Lieskovská, E., Jakubec, M., Jarina, R., & Chmulík, M. (2021) · Electronics · doi:10.3390/electronics10101163 · Cited by 246
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
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 233
Self-citationA Framework for Automatic Human Emotion Classification Using Emotion Profiles
Mower, E., Mataric, M. J., & Narayanan, S. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2010.2076804 · Cited by 215
Chung, Y.-A., Wu, C.-C., Shen, C.-H., Lee, H.-Y., & Lee, L.-S. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-82 · Cited by 214
Cukurova, M., Kent, C., & Luckin, R. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12829 · Cited by 222
Improved End-to-End Speech Emotion Recognition Using Self Attention Mechanism and Multitask Learning
Li, Y., Zhao, T., & Kawahara, T. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2594 · Cited by 197
Two-layer fuzzy multiple random forest for speech emotion recognition in human-robot interaction
Chen, L., Su, W., Yu, F., Wu, M., She, J., & Hirota, K. (2019) · Information Sciences · doi:10.1016/j.ins.2019.09.005 · Cited by 195
Survey 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
Survey of Deep Representation Learning for Speech Emotion Recognition
Latif, S., Rana, R. K., Khalifa, S., Jurdak, R., Qadir, J., & Schuller, B. W. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3114365 · Cited by 193
Self-citationConvolutional RNN: An enhanced model for extracting features from sequential data
Keren, G., & Schuller, B. W. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727636 · Cited by 172
Self-citationWöllmer, 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 165
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 167
Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data
Wagner, J., Andre, E., Lingenfelser, F., & Kim, J. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.12 · Cited by 156
An Image-based Deep Spectrum Feature Representation for the Recognition of Emotional Speech
Cummins, N., Amiriparian, S., Hagerer, G., Batliner, A. M., Steidl, S., & Schuller, B. W. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123371 · Cited by 155
Self-citationYao, Z., Wang, Z., Liu, W., Liu, Y., & Pan, J. (2020) · Speech Communication · doi:10.1016/j.specom.2020.03.005 · Cited by 152
Semisupervised Autoencoders for Speech Emotion Recognition
Deng, J., Xu, X., Zhang, Z., Frühholz, S., & Schuller, B. W. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2759338 · Cited by 152
Self-citationMultimodal Fusion of BERT-CNN and Gated CNN Representations for Depression Detection
Makiuchi, M. R., Warnita, T., Uto, K., & Shinoda, K. (2019) · doi:10.1145/3347320.3357694 · Cited by 158
Transfer Learning for Improving Speech Emotion Classification Accuracy
Latif, S., Rana, R. K., Younis, M. S., Qadir, J., & Epps, J. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1625 · Cited by 146
Major depressive disorder discrimination using vocal acoustic features
Taguchi, T., Tachikawa, H., Nemoto, K., Suzuki, M., Nagano, T., Tachibana, R., Nishimura, M., & Arai, T. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.08.038 · Cited by 144
Farooq, M., Hussain, F., Baloch, N. K., Raja, F. R., Yu, H., & Zikria, Y. B. (2020) · Sensors · doi:10.3390/s20216008 · Cited by 141
Zhao, Z., Bao, Z., Zhao, Y., Zhang, Z., Cummins, N., Ren, Z. X., & Schuller, B. W. (2019) · IEEE Access · doi:10.1109/access.2019.2928625 · Cited by 141
Self-citationEyben, 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-citationSchmitt, M., Ringeval, F., & Schuller, B. W. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1124 · Cited by 135
Self-citationSelf-Attention for Speech Emotion Recognition
Tarantino, L., Garner, P. N., & Lazaridis, A. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2822 · Cited by 137
Forbes-Riley, K., & Litman, D. J. (2011) · Speech Communication · doi:10.1016/j.specom.2011.02.006 · Cited by 132
Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems
Bertero, D., Siddique, F. B., Wu, C.-S., Wan, Y., Chan, R. H. Y., & Fung, P. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1110 · Cited by 131
Emotion Intensity and its Control for Emotional Voice Conversion
Zhou, K., Şişman, B., Rana, R. K., Schuller, B. W., & Li, H. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3175578 · Cited by 130
Self-citationProgressive Neural Networks for Transfer Learning in Emotion Recognition
Gideon, J., Khorram, S., Aldeneh, Z., Dimitriadis, D., & Provost, E. M. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-1637 · Cited by 129
Ranking-Based Emotion Recognition for Music Organization and Retrieval
Yang, Y., & Chen, H. H. (2010) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2010.2064164 · Cited by 131
Using regional saliency for speech emotion recognition
Aldeneh, Z., & Provost, E. M. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952655 · Cited by 126
Universum Autoencoder-Based Domain Adaptation for Speech Emotion Recognition
Deng, J., Xu, X., Zhang, Z., Fruhholz, S., & Schuller, B. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2672753 · Cited by 125
Self-citationRepresentation 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
McGinnis, E. W., Anderau, S. P., Hruschak, J. L., Gurchiek, R. D., Lopez‐Duran, N. L., Fitzgerald, K. D., Rosenblum, K. L., Muzik, M., & McGinnis, R. S. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2913590 · Cited by 131
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
Self-citationImproving Speech Emotion Recognition With Adversarial Data Augmentation Network
Lu, Y., & Mak, M. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027600 · Cited by 124
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
Semi-Supervised Speech Emotion Recognition With Ladder Networks
Parthasarathy, S., & Busso, C. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3023632 · Cited by 119
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 123
A first look into a Convolutional Neural Network for speech emotion detection
Bertero, D., & Fung, P. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7953131 · Cited by 117
Anger recognition in speech using acoustic and linguistic cues
Polzehl, T., Schmitt, A., Metze, F., & Wagner, M. (2011) · Speech Communication · doi:10.1016/j.specom.2011.05.002 · Cited by 121
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
Jiang, P., Fu, H., Tao, H., Lei, P., & Zhao, L. (2019) · IEEE Access · doi:10.1109/access.2019.2927384 · Cited by 108
Black, M. P., Katsamanis, A., Baucom, B., Lee, C.-C., Lammert, A. C., Christensen, A., Georgiou, P., & Narayanan, S. S. (2011) · Speech Communication · doi:10.1016/j.specom.2011.12.003 · Cited by 106
Multi-modal depression detection based on emotional audio and evaluation text
Ye, J., Yu, Y., Wang, Q., Li, W., Hu, L., Zheng, Y., & Fu, G. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.08.090 · Cited by 110
Zhao, Z., Li, Q., Zhang, Z., Cummins, N., Wang, H., Tao, J., & Schuller, B. W. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.03.013 · Cited by 105
Self-citationToward Automated Feedback on Teacher Discourse to Enhance Teacher Learning
Jensen, E., Dale, M., Donnelly, P. J., Stone, C., Kelly, S., GODLEY, A. J., & D’Mello, S. K. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376418 · Cited by 108
Ensemble CCA for Continuous Emotion Prediction
Kaya, H., Çilli, F., & Salah, A. A. (2014) · International Workshop on Audio/Visual Emotion Challenge (AVEC) · doi:10.1145/2661806.2661814 · Cited by 99
Towards a Small Set of Robust Acoustic Features for Emotion Recognition: Challenges
Tahon, M., & Devillers, L. Y. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2487051 · Cited by 98
ShEMO: a large-scale validated database for Persian speech emotion detection
Nezami, O. M., Lou, P. J., & Karami, M. (2018) · Language Resources and Evaluation · doi:10.1007/s10579-018-9427-x · Cited by 97
Gharavian, D., Sheikhan, M., Nazerieh, A., & Garoucy, S. (2011) · Neural Computing and Applications · doi:10.1007/s00521-011-0643-1 · Cited by 97
Cross-Corpus Speech Emotion Recognition Based on Domain-Adaptive Least-Squares Regression
Zong, Y., Zheng, W., Zhang, T., & Huang, X. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2537926 · Cited by 96
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
Glottal Source Information for Pathological Voice Detection
Narendra, N. P., & Alku, P. (2020) · IEEE Access · doi:10.1109/access.2020.2986171 · Cited by 89
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 108
Self-citationA Novel Speech Emotion Recognition Method via Incomplete Sparse Least Square Regression
Zheng, W., Xin, M., Wang, X., & Wang, B. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2308954 · Cited by 84
Predictive Models for Imbalanced Data: A School Dropout Perspective
Barros, T. M., Neto, P. A. S., da Silva, I. M. D., & Guedes, L. A. (2019) · Education Sciences · doi:10.3390/educsci9040275 · Cited by 83
A Review of Generalizable Transfer Learning in Automatic Emotion Recognition
Feng, K., & Chaspari, T. (2020) · Frontiers in Computer Science · doi:10.3389/fcomp.2020.00009 · Cited by 82
Peng, Z., Li, X., Zhu, Z., Unoki, M., Dang, J., & Akagi, M. (2020) · IEEE Access · doi:10.1109/access.2020.2967791 · Cited by 80
Exploring Automatic COVID-19 Diagnosis via Voice and Symptoms from Crowdsourced Data
Han, J., Brown, C., Chauhan, J., Grammenos, A., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., & Mascolo, C. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414576 · Cited by 85
Efficient and effective strategies for cross-corpus acoustic emotion recognition
Kaya, H., & Karpov, A. A. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.049 · Cited by 78
Classifying skewed data: importance weighting to optimize average recall
Rosenberg, A. (2012) · Interspeech (USB) · doi:10.21437/interspeech.2012-131 · Cited by 77
Speech Emotion Recognition Considering Nonverbal Vocalization in Affective Conversations
Hsu, J.-H., Su, M.-H., Wu, C., & Chen, Y. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3076364 · Cited by 78
Analysis and detection of cognitive load and frustration in drivers' speech
Bořil, H., Sadjadi, S. O., Kleinschmidt, T., & Hansen, J. H. L. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-208 · Cited by 77
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
Koike, T., Qian, K., Kong, Q., Plumbley, M. D., Schuller, B. W., & Yamamoto, Y. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175450 · Cited by 79
Self-citationUsing 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
Self-citationAttention-Enhanced Connectionist Temporal Classification for Discrete Speech Emotion Recognition
Zhao, Z., Bao, Z., Zhang, Z., Cummins, N., Wang, H., & Schuller, B. W. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1649 · Cited by 76
Self-citationKernelized Supervised Dictionary Learning
Gangeh, M. J., Ghodsi, A., & Kamel, M. S. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2274276 · Cited by 75
The 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
Self-citationMEC 2016: The Multimodal Emotion Recognition Challenge of CCPR 2016
Li, Y., Tao, J., Schuller, B. W., Shan, S., Jiang, D., & Jia, J. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3005-5_55 · Cited by 73
Self-citationA Multi-Scale Fusion Framework for Bimodal Speech Emotion Recognition
Chen, M., & Zhao, X. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-3156 · 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
Metadata
- Authors
- Schuller, B., Steidl, S., & Batliner, A.
- Affiliations
- Technical University of Munich (DE)
- MorphoSys (Germany) (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Venue
- Interspeech 2009
- Publisher
- ISCA
- Published
- 2009-09-06
- Pages
- 312-315
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W1501669607
- Cited by
- 938
- References
- 9
- Citations by year
- 2026: 34 · 2025: 35 · 2024: 25 · 2023: 38 · 2022: 50 · 2021: 77 · 2020: 84 · 2019: 79 · 2018: 80 · 2017: 64 · 2016: 64 · 2015: 56 · 2014: 53 · 2013: 47 · 2012: 40
- FWCI
- 87.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Funders
- European Commission (2007–2013, FP7 2007-2013, FP7/2007, 211486)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- emotion recognition from speech, benchmarking, spontaneous speech, room acoustics, speaker independence, feature types, emotion classification
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
