The INTERSPEECH 2009 emotion challenge

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

The relevance of feature type for the automatic classification of emotional user states: low level descriptors and functionals

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

Combining 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

A 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

Patterns, 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

Emotion Recognition in Public Speaking Scenarios Utilising An LSTM-RNN Approach with Attention

Baird, A., Amiriparian, S., Milling, M., & Schuller, B. W. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383542 · Cited by 16

Investigating Speech Enhancement and Perceptual Quality for Speech Emotion Recognition

Avila, A. R., Alam, J., O’Shaughnessy, D. D., & Falk, T. H. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-2350 · Cited by 16

Introduction to Emotion Recognition

Konar, A., Halder, A., & Chakraborty, A. (2015) · Wiley eBooks · doi:10.1002/9781118910566.ch1 · Cited by 17

Real-time automatic emotion recognition from speech

Vogt, T. (2010) · PUB – Publications at Bielefeld University (Bielefeld University) · Cited by 16

RANSAC-based training data selection for emotion recognition from spontaneous speech

Erdem, Ç. E., Bozkurt, E., Erzin, E., & Erdem, A. T. (2010) · International Workshop on Affective Interaction in Natural Environments (AFFINE) · doi:10.1145/1877826.1877831 · Cited by 15

On rater reliability and agreement based dynamic active learning

Zhang, Y., Coutinho, E., Schuller, B. W., Zhang, Z., Schuller, B. W., & Adam, M. G. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344553 · Cited by 15

Emotion recognition from speech under environmental noise conditions using wavelet decomposition

Vásquez-Correa, J. C., Garcia, N., Orozco‐Arroyave, J. R., Arias-Londoño, J. D., Vargas‐Bonilla, J. F., & Nöth, E. (2015) · IEEE International Carnahan Conference on Security Technology (ICCST) · doi:10.1109/ccst.2015.7389690 · Cited by 15

Posterior-thresholding feature extraction for paralinguistic speech classification

Gosztolya, G. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.104943 · Cited by 15

Cross-Modal Coordination of Face-Directed Gaze and Emotional Speech Production in School-Aged Children and Adolescents with ASD

Sorensen, T., Zane, E. R., Feng, T., Narayanan, S. S., & Grossman, R. B. (2019) · Scientific Reports · doi:10.1038/s41598-019-54587-z · Cited by 17

Is She Truly Enjoying the Conversation?

Katada, S., Okada, S., Hirano, Y., & Komatani, K. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3418844 · Cited by 15

Generating Robotic Speech Prosody for Human Robot Interaction: A Preliminary Study

Lee, J. (2021) · Applied Sciences · doi:10.3390/app11083468 · Cited by 15

Evaluating a Spoken Dialogue System that Detects and Adapts to User Affective States

Litman, D. J., & Forbes-Riley, K. (2014) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.3115/v1/w14-4324 · Cited by 15

CINEMO — A French Spoken Language Resource for Complex Emotions: Facts and Baselines

Schuller, B. W., Zaccarelli, R., Rollet, N., & Devillers, L. Y. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4ifzegtz62vd · Cited by 15

Improving generation performance of speech emotion recognition by denoising autoencoders

Chao, L., Tao, J., Yang, M., & Li, Y. (2014) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp.2014.6936627 · Cited by 16

Automatic identification of salient acoustic instances in couples' behavioral interactions using diverse density support vector machines

Gibson, J., Katsamanis, A., Black, M. P., & Narayanan, S. S. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-470 · Cited by 15

Knowledge-based Framework for Intelligent Emotion Recognition in Spontaneous Speech

Chakraborty, R., Pandharipande, M. A., & Kopparapu, S. K. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.08.239 · Cited by 16

MES-P: An Emotional Tonal Speech Dataset in Mandarin with Distal and Proximal Labels

Xiao, Z., Chen, Y., Dou, W., Tao, Z., & Chen, L. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2945322 · Cited by 15

Building a Robust System for Multimodal Emotion Recognition

Wagner, J., Lingenfelser, F., & André, E. (2015) · Wiley eBooks · doi:10.1002/9781118910566.ch15 · Cited by 16

Machine learning for the recognition of emotion in the speech of couples in psychotherapy using the Stanford Suppes Brain Lab Psychotherapy DatasetPreprint

Crangle, C., Wang, R., Guimaraes, M. P., Nguyen, M., Nguyen, D. T., & Suppes, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.04110 · Cited by 15

Speech Emotion Recognition System: A ReviewPreprint

Shinde, A. S., & Patil, V. V. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3869462 · Cited by 15

Spectrogram Features for Audio and Speech Analysis

McLoughlin, I. V., Pham, L. D., Song, Y., Miao, X., Phan, H., Cai, P., Gu, Q., Nan, J., Song, H., & Soh, D. C. L. (2026) · Applied Sciences · doi:10.3390/app16020572 · Cited by 14

Cross-corpus analysis for acoustic recognition of negative interactions

Lefter, I., Nefs, H. T., Jonker, C. M., & Rothkrantz, L. J. M. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344562 · Cited by 14

Enhanced detection of early Parkinson’ s disease through multi-sensor fusion on smartphone-based IoMT platforms

He, T., Chen, J., Hossain, M. S., & Lv, Z. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102889 · Cited by 14

Emotion recognition from speech: WOC-NN and class-interaction

Attabi, Y., & Dumouchel, P. (2012) · International Conference on Information Science, Signal Processing and their Applications (ISSPA) · doi:10.1109/isspa.2012.6310487 · Cited by 14

Speech emotion recognition with cross-lingual databases

Chiou, B.-C., & Chen, C.-P. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-136 · Cited by 14

Improved semi-supervised autoencoder for deception detection

Fu, H., Lei, P., Tao, H., Zhao, L., & Yang, J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0223361 · Cited by 14

Transfer Learning in Speaker’s Age and Gender Recognition

Markitantov, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60276-5_32 · Cited by 14

Semi-supervised parallel shared encoders for speech emotion recognition

Pourebrahim, Y., Razzazi, F., & Sameti, H. (2021) · Digital Signal Processing · doi:10.1016/j.dsp.2021.103205 · Cited by 14

Multi-Local Attention for Speech-Based Depression Detection

Tao, F., Ge, X., Ma, W., Esposito, A. A., & Vinciarelli, A. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095757 · Cited by 14

Leveraging valence and activation information via multi-task learning for categorical emotion recognition

Xia, R., & Liu, Y. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178983 · Cited by 14

Preliminary Arabic speech emotion classification

Meftah, A. H., Selouani, S., & Alotaibi, Y. A. (2014) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2014.7300584 · Cited by 14

Transfer Learning of Deep Neural Network for Speech Emotion Recognition

Huang, Y., Hu, M., Yu, X., Wang, T., & Yang, C. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3005-5_59 · Cited by 14

Construction and analysis of Indonesian Emotional Speech Corpus

Lubis, N., Lestari, D. P., Purwarianti, A., Sakti, S., & Nakamura, S. (2014) · International Conference on Speech Database and Assessments · doi:10.1109/icsda.2014.7051413 · Cited by 14

Emotional speech recognition: A multilingual perspective

Meftah, A. H., Alotaibi, Y. A., & Selouani, S. (2016) · International Conference on Bio-engineering for Smart Technologies (BioSMART) · doi:10.1109/biosmart.2016.7835600 · Cited by 14

Supra‐segmental Features

Schuller, B. W., & Batliner, A. M. (2013) · Wiley eBooks · doi:10.1002/9781118706664.ch10 · Cited by 14

Automatic detection of expressed emotion from Five-Minute Speech Samples: Challenges and opportunities

Mirheidari, B., Bittar, A., Cummins, N., Downs, J. M., Fisher, H. L., & Christensen, H. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300518 · Cited by 13

Ten Recent Trends in Computational Paralinguistics

Schuller, B. W., & Weninger, F. J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34584-5_3 · Cited by 13

Towards distributed recognition of emotion from speech

Han, W., Zhang, Z., Deng, J., Wöllmer, M., Weninger, F. J., & Schuller, B. W. (2012) · International Symposium on Communications, Control and Signal Processing · doi:10.1109/isccsp.2012.6217769 · Cited by 13

An Expectation Maximization Approach to Joint Modeling of Multidimensional Ratings Derived from Multiple Annotators

Ramakrishna, A., Gupta, R., Grossman, R. B., & Narayanan, S. S. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-270 · Cited by 13

RUC at MediaEval 2016 Emotional Impact of Movies Task: Fusion of Multimodal Features.

Chen, S., & Jin, Q. (2016) · MediaEval · Cited by 13

Automatic individual dog recognition based on the acoustic properties of its barks

Pérez-Espinosa, H., Reyes‐Meza, V., Aguilar-Benitez, E., & Sanzón-Rosas, Y. M. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169509 · Cited by 13

Spatio-Temporal Context Modelling for Speech Emotion Classification

Jalal, M. A., Moore, R. K., & Hain, T. (2019) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru46091.2019.9004037 · Cited by 13

Significance of Phonological Features in Speech Emotion Recognition

Wang, W., Watters, P., Cao, X., Shen, L., & Li, B. (2020) · International Journal of Speech Technology · doi:10.1007/s10772-020-09734-7 · Cited by 13

Double sparse learning model for speech emotion recognition

Zong, Y., Zheng, W., Cui, Z., & Li, Q. (2016) · Electronics Letters · doi:10.1049/el.2016.1211 · Cited by 13

Emotional Speech of Mentally and Physically Disabled Individuals: Introducing the EmotAsS Database and First Findings

Hantke, S., Sagha, H., Cummins, N., & Schuller, B. W. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-409 · Cited by 13

An Agreement and Sparseness-based Learning Instance Selection and its Application to Subjective Speech Phenomena

Zhang, Z., Eyben, F., Deng, J. D., & Schuller, B. W. (2014) · OPUS (Augsburg University) · Cited by 13

More Than Just Words

Yang, L., Wang, Y., Dunne, D., Sobolev, M., Naaman, M., & Estrin, D. L. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3290993 · Cited by 18

Articulation constrained learning with application to speech emotion recognition

Shah, M., Tu, M., Berisha, V., Chakrabarti, C., & Spanias, A. (2019) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-019-0157-9 · Cited by 13

Automatic children’s personality assessment from emotional speech

Pérez-Espinosa, H., Gutiérrez-Serafín, B., Martı́nez-Miranda, J., & Espinosa‐Curiel, I. E. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115885 · Cited by 13

Toward robust learning of the Gaussian mixture state emission densities for hidden Markov models

Tang, H., Hasegawa‐Johnson, M., & Huang, T. S. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5494989 · Cited by 13

Development and evaluation of a hand tracker using depth images captured from an overhead perspective

Czarnuch, S. M., & Mihailidis, A. (2015) · Disability and Rehabilitation Assistive Technology · doi:10.3109/17483107.2015.1027304 · Cited by 13

Towards friendly AI: a comprehensive review and new perspectives on human-AI alignment

Sun, Q., Li, Y., Alturki, E., Murthy, S. M. K., & Schuller, B. W. (2026) · AI and Ethics · doi:10.1007/s43681-026-01070-x · Cited by 18

the Munich open Speech and Music Interpretation by Large Space Extraction toolkit

Eyben, F., Wöllmer, M., & Schuller, B. W. (2010) · Cited by 12

Synchronous prediction of arousal and valence using LSTM network for affective video content analysis

Zhang, L., & Zhang, J. (2017) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2017.8393364 · Cited by 13

Acoustic differences in emotional speech of people with dysarthria

Alhinti, L., Christensen, H., & Cunningham, S. P. (2020) · Speech Communication · doi:10.1016/j.specom.2020.11.005 · Cited by 12

Language Model-Based Emotion Prediction Methods for Emotional Speech Synthesis Systems

Yoon, H.-W., Kwon, O., Lee, H., Yamamoto, R., Song, E., Kim, J., & Hwang, M.-J. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-11133 · Cited by 17

Automatic Parliamentary Meeting Minute Generation Using Rhetorical Structure Modeling

Zhang, J. J., & Fung, P. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2012.2215592 · Cited by 12

Protocol and baseline for experiments on Bogazici University Turkish emotional speech corpus

Kaya, H., Salah, A. A., Gürgen, F. S., & Ekenel, H. K. (2014) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2014.6830575 · Cited by 12

Emotion recognition on Indonesian television talk shows

Lubis, N., Lestari, D. P., Purwarianti, A., Sakti, S., & Nakamura, S. (2014) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2014.7078619 · Cited by 12

Opening big in box office? Trailer content can help

Tadimari, A., Kumar, N., Guha, T., & Narayanan, S. S. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472183 · Cited by 12

Unsatisfied customer call detection with deep learning

Cong, P., Wang, C., Ren, Z., Wang, H., Wang, Y., & Feng, J. (2016) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp.2016.7918385 · Cited by 12

Wavelet-Based Time-Frequency Representations for Automatic Recognition of Emotions from Speech.

Vásquez-Correa, J. C., Arias‐Vergara, T., Orozco‐Arroyave, J. R., Vargas‐Bonilla, J. F., & Nöth, E. (2016) · ITG Symposium of Speech Communication · Cited by 12

Speech emotion recognition with ensemble learning methods

Shih, P.-Y., Chen, C.-P., & Wu, C. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952658 · Cited by 12

Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation

Kun, Q., Janott, C., Zixing, Z., Jun, D., Baird, A., Heiser, C., Hohenhorst, W., Herzog, M., Hemmert, W., & Schuller, B. W. (2018) · Archives of Acoustics · doi:10.24425/123918 · Cited by 12

Automatic voice emotion recognition of child-parent conversations in natural settings

Law, E. L., Soleimani, S., Watkins, D., & Barwick, J. (2020) · Behaviour and Information Technology · doi:10.1080/0144929x.2020.1741684 · Cited by 12

Learning Salient Segments for Speech Emotion Recognition Using Attentive Temporal Pooling

Xia, X., Jiang, D., & Hichem Sahli, B. (2020) · IEEE Access · doi:10.1109/access.2020.3014733 · Cited by 12

Late fusion of individual engines for improved recognition of negative emotion in speech - learning vs. democratic vote

Schuller, B. W., Metze, F., Steidl, S., Batliner, A. M., Eyben, F., & Polzehl, T. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5494986 · Cited by 12

Towards measuring similarity between emotional corpora

Brendel, M., Zaccarelli, R., Schuller, B. W., & Devilliers, L. (2010) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 12

Speech Emotion Recognition Based on Temporal-Spatial Learnable Graph Convolutional Neural Network

Yan, J., Li, H., Xu, F., Zhou, X., Liu, Y., & Yang, Y. (2024) · Electronics · doi:10.3390/electronics13112010 · Cited by 11

Enhanced face/audio emotion recognition

Torres, J. M. M., & Stepanov, E. A. (2017) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3106426.3109423 · Cited by 11

A Bimodal Approach for Speech Emotion Recognition using Audio and Text.

Verkholyak, O., Dvoynikova, A., & Karpov, A. A. (2021) · Journal of Internet Services and Information Security · Cited by 11

Detecting System Alzheimer’s Dementia by 1d CNN-LSTM in Japanese Speech

Nishikawa, K., Hirakawa, R., Kawano, H., Nakashi, K., & Nakatoh, Y. (2021) · International Conference on Consumer Electronics · doi:10.1109/icce50685.2021.9427692 · Cited by 11

End-to-End Model-Based Detection of Infants with Autism Spectrum Disorder Using a Pretrained Model

Lee, J. H., Lee, G. W., Bong, G., Yoo, H. J., & Kim, H. K. (2022) · Sensors · doi:10.3390/s23010202 · Cited by 11

On the use of speech parameter contours for emotion recognition

Sethu, V., Eliathamby Ambikairajah, A., & Epps, J. (2013) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/1687-4722-2013-19 · Cited by 11

Real-time robust recognition of speakers' emotions and characteristics on mobile platforms

Eyben, F., Huber, B., Marchi, E., Schuller, D., & Schuller, B. W. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344658 · Cited by 11

A PLLR and multi-stage Staircase Regression framework for speech-based emotion prediction

Huang, Z., & Epps, J. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953137 · Cited by 11

What Can Self-Reports and Acoustic Data Analyses on Emotions Tell Us?

Soleimani, S., & Law, E. L. (2017) · Designing Interactive Systems Conference · doi:10.1145/3064663.3064770 · Cited by 15

Multimodal Indicators of Humor in Videos

Yang, Z., Ai, L., & Hirschberg, J. (2019) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2019.00109 · Cited by 11

An End-to-end Multitask Learning Model to Improve Speech Emotion Recognition

Fu, C., Liu, C., Ishi, C. T., & Ishiguro, H. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287484 · Cited by 11

Chinese Multimodal Emotion Recognition in Deep and Traditional Machine Leaming Approaches

Miao, H., Zhang, Y., Li, W., Zhang, H., Wang, D., & Feng, S. (2018) · Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) · doi:10.1109/aciiasia.2018.8470379 · Cited by 11

Over-Sampling Emotional Speech Data Based on Subjective Evaluations Provided by Multiple Individuals

Lotfian, R., & Busso, C. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2901465 · Cited by 11

Spotting the Traces of Depression in Read Speech: An Approach Based on Computational Paralinguistics and Social Signal Processing

Tao, F., Esposito, A. A., & Vinciarelli, A. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2888 · Cited by 11

Investigating Acoustic and Psycholinguistic Predictors of Cognitive Impairment in Older Adults: Modeling Study

Badal, V. D., Reinen, J., Twamley, E. W., Lee, E., Fellows, R. P., Bilal, E., & Depp, C. A. (2024) · JMIR Aging · doi:10.2196/54655 · Cited by 13

Leveraging Content and Acoustic Representations for Speech Emotion Recognition

Dutta, S., & Ganapathy, S. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3603853 · Cited by 14

Review and Comparative Analysis of Databases for Speech Emotion Recognition

Serrano, S., Serghini, O., Esposito, G., Carbone, S., Mento, C., Floris, A., Porcu, S., & Atzori, L. (2025) · International Conference on Data Technologies and Applications · doi:10.3390/data10100164 · Cited by 8

Continuous emotion tracking using total variability space

Khaki, H., & Erzin, E. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-324 · Cited by 10

Multi-layer maximum mean discrepancy in auto-encoders for cross-corpus speech emotion recognition

Nasersharif, B., Ebrahimpour, M., & Naderi, N. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05161-y · Cited by 10

Multimodal Emotion Recognition from Voice and Video Signals

Barra, P., Mnasri, Z., & Greco, D. (2023) · IEEE EUROCON - International Conference on Computer as a Tool (EUROCON) · doi:10.1109/eurocon56442.2023.10198928 · Cited by 10

Non-invasive real-time multimodal deception detection using machine learning and parallel computing techniques

Abdulridha, F., & Albaker, B. M. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01255-4 · Cited by 10

Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection

Niizumi, D., Takeuchi, D., Ohishi, Y., Harada, N., & Kashino, K. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782479 · Cited by 10

Continuous Mapping of Personality Traits

Kaya, H., & Salah, A. A. (2014) · doi:10.1145/2668024.2668025 · Cited by 10

Data selection for acoustic emotion recognition: Analyzing and comparing utterance and sub-utterance selection strategies

Le, D., & Provost, E. M. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344564 · Cited by 10

Detection of emotionally significant regions of speech for emotion recognition

Vydana, H. K., Vikash, P., Vamsi, T., Kumar, K. P., & Vuppala, A. K. (2015) · IEEE India Conference · doi:10.1109/indicon.2015.7443415 · Cited by 10

Audio-Based Granularity-Adapted Emotion Classification

Shepstone, S. E., Tan, Z., & Jensen, S. H. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2598741 · Cited by 10

Emotion Recognition Using Support Vector Machine and Deep Neural Network

Chen, R., Zhou, Y., & Qian, Y. (2018) · Communications in computer and information science · doi:10.1007/978-981-10-8111-8_12 · Cited by 10

Video and Text-Based Affect Analysis of Children in Play Therapy

Doyran, M., Türkmen, B., Oktay, E. A., Halfon, S., & Salah, A. A. (2019) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3340555.3353760 · Cited by 10

Can Appliances Understand the Behavior of Elderly Via Machine Learning? A Feasibility Study

Qian, K., Koike, T., Yoshiuchi, K., Schuller, B. W., & Yamamoto, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3045009 · Cited by 10

Transferring Cross-Corpus Knowledge: An Investigation on Data Augmentation for Heart Sound Classification

Koike, T., Qian, K., Schuller, B. W., & Yamamoto, Y. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629714 · Cited by 10

Feature selection enhancement and feature space visualization for speech-based emotion recognition

Kanwal, S., Asghar, S., & Ali, H. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1091 · Cited by 10

Comparison of Gender- and Speaker-adaptive Emotion Recognition

Yuryevich, S. M., Ultes, S., & Schmitt, A. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4wxqwzrhi4t2 · Cited by 10

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Schuller, B. W., Batliner, A. M., Steidl, S., & Seppi, D. (2011) · Speech Communication · doi:10.1016/j.specom.2011.01.011 · Cited by 765

GTM-URL contribution to the INTERSPEECH 2009 emotion challenge

Planet, S., Iriondo, I., Socoró, J. C., Monzó, C., & Adell, J. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-104 · Cited by 21

Towards More Reality in the Recognition of Emotional Speech

Schuller, B. W., Seppi, D., Batliner, A. M., Maier, A., & Steidl, S. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.367226 · Cited by 124

INTERSPEECH 2009 Emotion Recognition Challenge evaluation

Bozkurt, E., Erzin, E., Erdem, Ç. E., & Erdem, A. T. (2010) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2010.5649919 · Cited by 8

Cross-corpus acoustic emotion recognition: Variances and strategies (Extended abstract)

Schuller, B. W., Vlasenko, B., Eyben, F., Wöllmer, M., Stuhlsatz, A., Wendemuth, A., & Rigoll, G. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344612 · Cited by 7