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

Exploitation of Phase-Based Features for Whispered Speech Emotion Recognition

Deng, J., Xu, X.-Z., Zhang, Z.-X., Fruhholz, S., & Schuller, B. (2016) · IEEE Access · doi:10.1109/access.2016.2591442 · Cited by 36

Face mask recognition from audio: The MASC database and an overview on the mask challenge

Mohamed, M. M., Nessiem, M. A., Batliner, A. M., Bergler, C., Hantke, S., Schmitt, M., Baird, A., Mallol-Ragolta, A., Karas, V., Amiriparian, S., et al. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108361 · Cited by 38

Recognising interest in conversational speech - comparing bag of frames and supra-segmental features

Schuller, B. W., & Rigoll, G. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-484 · Cited by 38

Inference of Human Beings’ Emotional States from Speech in Human–Robot Interactions

Devillers, L. Y., Tahon, M., Sehili, M. A., & Delaborde, A. (2015) · International Journal of Social Robotics · doi:10.1007/s12369-015-0297-8 · Cited by 38

The hinterland of emotions: Facing the open-microphone challenge

Steidl, S., Batliner, A. M., Schuller, B. W., & Seppi, D. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349499 · Cited by 37

Deep Learning of Segment-Level Feature Representation with Multiple Instance Learning for Utterance-Level Speech Emotion Recognition

Mao, S., Ching, P. C., & Lee, T. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1968 · Cited by 37

Excitation Features of Speech for Emotion Recognition Using Neutral Speech as Reference

Kadiri, S. R., Gangamohan, P., Gangashetty, S. V., Alku, P., & Yegnanarayana, B. (2020) · Circuits Systems and Signal Processing · doi:10.1007/s00034-020-01377-y · Cited by 37

Can Machine Learning Assist Locating the Excitation of Snore Sound? A Review

Qian, K., Janott, C., Schmitt, M., Zhang, Z., Heiser, C., Hemmert, W., Yamamoto, Y., & Schuller, B. W. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3012666 · Cited by 37

Unsupervised feature selection and NMF de-noising for robust Speech Emotion Recognition

Bandela, S. R., & Kumar, T. K. (2020) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107645 · Cited by 37

CyTex: Transforming speech to textured images for speech emotion recognition

Bakhshi, A., Harimi, A., & Chalup, S. K. (2022) · Speech Communication · doi:10.1016/j.specom.2022.02.007 · Cited by 37

Continuous emotion recognition with phonetic syllables

Origlia, A., Cutugno, F., & Galatà, V. (2013) · Speech Communication · doi:10.1016/j.specom.2013.09.012 · Cited by 37

Improving automatic emotion recognition from speech signals

Bozkurt, E., Erzin, E., Erdem, Ç. E., & Erdem, A. T. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-106 · Cited by 36

A Bag of Wavelet Features for Snore Sound Classification

Qian, K., Schmitt, M., Janott, C., Zhang, Z., Heiser, C., Hohenhorst, W., Herzog, M., Hemmert, W., & Schuller, B. W. (2019) · Annals of Biomedical Engineering · doi:10.1007/s10439-019-02217-0 · Cited by 37

Completely Unsupervised Phoneme Recognition by Adversarially Learning Mapping Relationships from Audio Embeddings

Liu, D.-R., Chen, K., Lee, H.-Y., & Lee, L.-S. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1800 · Cited by 36

Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis

Li, J., Zhang, Z., Lang, J., Jiang, Y., An, L., Zou, P., Xu, Y., Gao, S., Lin, J., Fan, C., et al. (2022) · doi:10.1145/3551876.3554809 · Cited by 38

Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For RobustnessPreprint

Latif, S., Rana, R. K., & Qadir, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.11402 · Cited by 36

Cross Corpus Speech Emotion Recognition using transfer learning and attention-based fusion of Wav2Vec2 and prosody features

Naderi, N., & Nasersharif, B. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110814 · Cited by 35

Automatic Speech Emotion Recognition: A survey

Chandrasekar, P., Chapaneri, S., & Jayaswal, D. (2014) · International Conference on Communication System, Computing and IT Applications (CSCITA) · doi:10.1109/cscita.2014.6839284 · Cited by 35

Voice and Speech Analysis in Search of States and Traits

Schuller, B. W. (2011) · Springer eBooks · doi:10.1007/978-0-85729-994-9_9 · Cited by 35

Novel feature representation using single frequency filtering and nonlinear energy operator for speech emotion recognition

Thirumuru, R., Gurugubelli, K., & Vuppala, A. K. (2021) · Digital Signal Processing · doi:10.1016/j.dsp.2021.103293 · Cited by 35

End-to-End Modeling and Transfer Learning for Audiovisual Emotion Recognition in-the-Wild

Dresvyanskiy, D., Рюмина, Е. В., Kaya, H., Markitantov, M., Karpov, A. A., & Minker, W. (2022) · Multimodal Technologies and Interaction · doi:10.3390/mti6020011 · Cited by 35

Emotion recognition using imperfect speech recognition

Metze, F., Batliner, A. M., Eyben, F., Polzehl, T., Schuller, B. W., & Steidl, S. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-202 · Cited by 35

Multi-modal Emotion Recognition Using Canonical Correlations and Acoustic Features

Gajšek, R., Štruc, V., & Mihelič, F. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.1005 · Cited by 35

MERBench: A Unified Evaluation Benchmark for Multimodal Emotion Recognition

Lian, Z., Sun, L., Ren, Y., Gu, H., Sun, H., Chen, L., Liu, B., & Tao, J. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3653457 · Cited by 71

Dynamic estimation of worker reliability in crowdsourcing for regression tasks: Making it work

Tarasov, A., Delany, S. J., & Namee, B. M. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.04.012 · Cited by 34

A personalized emotion recognition system using an unsupervised feature adaptation scheme

Rahman, T., & Busso, C. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6289072 · Cited by 34

Review on Speech Emotion Recognition

Wen, H. (2014) · Cited by 34

The PInSoRo dataset: Supporting the data-driven study of child-child and child-robot social dynamics

Lemaignan, S., Edmunds, C. E. R., Senft, E., & Belpaeme, T. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205999 · Cited by 34

Improved Speaker Independent Dysarthria Intelligibility Classification Using Deepspeech Posteriors

Tripathi, A., Bhosale, S., & Kopparapu, S. K. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054492 · Cited by 35

Attention and Feature Selection for Automatic Speech Emotion Recognition Using Utterance and Syllable-Level Prosodic Features

Alex, S. B., Mary, L., & Babu, B. P. (2020) · Circuits Systems and Signal Processing · doi:10.1007/s00034-020-01429-3 · Cited by 34

Learning Class-Aligned and Generalized Domain-Invariant Representations for Speech Emotion Recognition

Xiao, Y., Zhao, H., & Li, T. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2020.2972926 · Cited by 34

Domain adaptation for speech emotion recognition by sharing priors between related source and target classes

Mao, Q., Xue, W., Rao, Q., Zhang, F., & Zhan, Y. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472149 · Cited by 34

Emotion in the singing voice—a deeperlook at acoustic features in the light ofautomatic classification

Eyben, F., Salomão, G. L., Sundberg, J., Scherer, K. R., & Schuller, B. W. (2015) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-015-0057-6 · Cited by 33

Construction and analysis of phonetically and prosodically balanced emotional speech database

Takeishi, E., Nose, T., Chiba, Y., & Ito, A. (2016) · International Conference on Speech Database and Assessments · doi:10.1109/icsda.2016.7918977 · Cited by 33

Semi-supervised Ladder Networks for Speech Emotion Recognition

Tao, J.-H., Huang, J., Li, Y., Lian, Z., & Niu, M. (2019) · Machine Intelligence Research · doi:10.1007/s11633-019-1175-x · Cited by 33

Multimodal Sentiment Analysis based on Recurrent Neural Network and Multimodal Attention

Cai, C., He, Y. L., Sun, L., Lian, Z., Liu, B., Tao, J., Xu, M., & Wang, K. (2021) · doi:10.1145/3475957.3484454 · Cited by 33

Speech based Emotion Recognition based on hierarchical decision tree with SVM, BLG and SVR classifiers

Garg, V., Kumar, H., & Sinha, R. (2013) · National Conference on Communications · doi:10.1109/ncc.2013.6487987 · Cited by 33

Speech Based Emotion Recognition

Sethu, V., Epps, J., & Eliathamby Ambikairajah, A. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1456-2_7 · Cited by 32

Detection of negative emotions in speech signals using bags-of-audio-words

Pokorny, F. B., Graf, F., Pernkopf, F., & Schuller, B. W. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344678 · Cited by 32

Fisher Kernels on Phase-Based Features for Speech Emotion Recognition

Deng, J., Xu, X., Zhang, Z., Frühholz, S., Grandjean, D., & Schuller, B. W. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-2585-3_15 · Cited by 32

What is my Dog Trying to Tell Me? the Automatic Recognition of the Context and Perceived Emotion of Dog Barks

Hantke, S., Cummins, N., & Schuller, B. W. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461757 · Cited by 32

Convolutional Bidirectional Long Short-Term Memory for Deception Detection With Acoustic Features

Xie, Y., Liang, R., Tao, H., Zhu, Y., & Zhao, L. (2018) · IEEE Access · doi:10.1109/access.2018.2882917 · Cited by 32

Recognition of Students’ Mental States in Discussion Based on Multimodal Data and its Application to Educational Support

Peng, S., & Nagao, K. (2021) · IEEE Access · doi:10.1109/access.2021.3054176 · Cited by 33

Few-Shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network With Adaptive Sample Pair Formation

Feng, K., & Chaspari, T. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3109485 · Cited by 32

Attention gated tensor neural network architectures for speech emotion recognition

Pandey, S., Shekhawat, H. S., & Prasanna, S. R. M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103173 · Cited by 32

TalkTive: A Conversational Agent Using Backchannels to Engage Older Adults in Neurocognitive Disorders Screening

Ding, Z., Kang, J., Tinky Oi Ting, H. O., Wong, K. H., Fung, H. H.-L., Meng, H. M. L., & Ma, X. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502005 · Cited by 40

A Comparison of Machine Learning Algorithms and Feature Sets for Automatic Vocal Emotion Recognition in Speech

Doğdu, C., Kessler, T., Schneider, D., Shadaydeh, M., & Schweinberger, S. R. (2022) · Sensors · doi:10.3390/s22197561 · Cited by 32

Recognition of Nonprototypical Emotions in Reverberated and Noisy Speech by Nonnegative Matrix Factorization

Weninger, F. J., Schuller, B. W., Batliner, A. M., Steidl, S., & Seppi, D. (2011) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2011/838790 · Cited by 32

Recognizing affect from speech prosody using hierarchical graphical models

Fernandez, R., & Picard, R. W. (2011) · Speech Communication · doi:10.1016/j.specom.2011.05.003 · Cited by 35

Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

Wang, K.-C. (2015) · Sensors · doi:10.3390/s150101458 · Cited by 32

Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Recurrent Neural NetworksPreprint

Chung, Y.-A., Wu, C.-C., Shen, C.-H., & Lee, H.-Y. (2016) · arXiv (Cornell University) · Cited by 32

DEP-Former: Multimodal Depression Recognition Based on Facial Expressions and Audio Features via Emotional Changes

Ye, J., Yu, Y., Lu, L., Wang, H., Zheng, Y., Liu, Y., & Wang, Q. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3491098 · Cited by 33

Speech Emotion Classification and Public Speaking Skill Assessment

Pfister, T., & Robinson, P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14715-9_15 · Cited by 32

Learning with synthesized speech for automatic emotion recognition

Schuller, B. W., & Burkhardt, F. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5495017 · Cited by 31

Hilbert-envelope features for cardiac disease classification from noisy phonocardiograms

Nizam, N. B., Nuhash, S. I. S. K., & Hasan, T. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103864 · Cited by 30

Classifying turn-level uncertainty using word-level prosody

Litman, D. J., Rotaru, M., & Nicholas, G. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-577 · Cited by 30

Linked Source and Target Domain Subspace Feature Transfer Learning -- Exemplified by Speech Emotion Recognition

Deng, J., Zhang, Z., & Schuller, B. W. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.141 · Cited by 30

Towards Intelligent Crowdsourcing for Audio Data Annotation: Integrating Active Learning in the Real World

Hantke, S., Zhang, Z., & Schuller, B. W. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-406 · Cited by 30

Usual voice quality features and glottal features for emotional valence detection

Tahon, M., Degottex, G., & Devillers, L. Y. (2012) · Speech prosody · doi:10.21437/speechprosody.2012-173 · Cited by 30

Predicting Latent Narrative Mood Using Audio and Physiologic Data

Alhanai, T., & Ghassemi, M. M. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10625 · Cited by 33

Speech emotion classification using combined neurogram and INTERSPEECH 2010 paralinguistic challenge features

Jassim, W. A., Paramesran, R., & Harte, N. (2017) · IET Signal Processing · doi:10.1049/iet-spr.2016.0336 · Cited by 30

CAST a database: Rapid targeted large-scale big data acquisition via small-world modelling of social media platforms

Amiriparian, S., Pugachevskiy, S., Cummins, N., Hantke, S., Pohjalainen, J., Keren, G., & Schuller, B. W. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273622 · Cited by 29

Speech Emotion Recognition Based on Selective Interpolation Synthetic Minority Over-Sampling Technique in Small Sample Environment

Liu, Z., Wu, B.-H., Li, D., Xiao, P., & Mao, J.-W. (2020) · Sensors · doi:10.3390/s20082297 · Cited by 29

Robust Semisupervised Generative Adversarial Networks for Speech Emotion Recognition via Distribution Smoothness

Zhao, H., Xiao, Y., & Zhang, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3000751 · Cited by 29

Ensembling End-to-End Deep Models for Computational Paralinguistics Tasks: ComParE 2020 Mask and Breathing Sub-Challenges

Markitantov, M., Dresvyanskiy, D., Mamontov, D., Kaya, H., Minker, W., & Karpov, A. A. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2666 · Cited by 29

Detecting Expressions with Multimodal Transformers

Parthasarathy, S., & Sundaram, S. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383573 · Cited by 29

Investigation and Evaluation of Glottal Flow Waveform for Voice Pathology Detection

Wu, Y., Zhou, C., Fan, Z., Wu, D., Zhang, X., & Tao, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3046767 · Cited by 29

BLSTM and CNN Stacking Architecture for Speech Emotion Recognition

Li, D., Sun, L., Xu, X., Wang, Z., Zhang, J., & Du, W. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10581-z · Cited by 30

Acoustic Features and Neural Representations for Categorical Emotion Recognition from Speech

Keesing, A., Koh, Y. S., & Witbrock, M. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-2217 · Cited by 29

Effects of Physiological Signals in Different Types of Multimodal Sentiment Estimation

Katada, S., Okada, S., & Komatani, K. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3155604 · Cited by 29

Automatic Speech Emotion Recognition of Younger School Age Children

Matveev, Y. N., Matveev, A., Frolova, O. V., Lyakso, E. E., & Nersisson, R. (2022) · Mathematics · doi:10.3390/math10142373 · Cited by 29

Multi-Stream Convolution-Recurrent Neural Networks Based on Attention Mechanism Fusion for Speech Emotion Recognition

Tao, H., Geng, L., Shan, S., Mai, J., & Fu, H. (2022) · Entropy · doi:10.3390/e24081025 · Cited by 29

Making music through real-time voice timbre analysis: machine learning and timbral control

Stowell, D. (2010) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 29

Surfboard: Audio Feature Extraction for Modern Machine Learning

Lenain, R., Weston, J., Shivkumar, A., & Fristed, E. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2879 · Cited by 33

Computer Audition for Fighting the SARS-CoV-2 Corona Crisis—Introducing the Multitask Speech Corpus for COVID-19

Qian, K., Schmitt, M., Zheng, H., Koike, T., Han, J., Liu, J., Ji, W., Duan, J., Song, M., Yang, Z., et al. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3067605 · Cited by 28

Multistage linguistic conditioning of convolutional layers for speech emotion recognition

Triantafyllopoulos, A., Reichel, U. D., Liu, S., Huber, S., Eyben, F., & Schuller, B. W. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1072479 · Cited by 29

Comparison between decision-level and feature-level fusion of acoustic and linguistic features for spontaneous emotion recognition

Planet, S., & Iriondo, I. (2012) · Iberian Conference on Information Systems and Technologies (CISTI) · Cited by 28

Anger detection in call center dialogues

Pappas, D., Androutsopoulos, I., & Papageorgiou, H. (2015) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2015.7390579 · Cited by 28

Developing an AI-Assisted Low-Resource Spoken Language Learning App for Children

Getman, Y., Phan, N., Al-Ghezi, R., Voskoboinik, E., Singh, M., Grósz, T., Kurimo, M., Salvi, G., Svendsen, T., Strömbergsson, S., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3304274 · Cited by 27

Audiovisual three-level fusion for continuous estimation of Russell's emotion circumplex

Sánchez, E., Lopez‐Otero, P., Docío-Fernández, L., Argones-Rúa, E., & Alba‐Castro, J. L. (2013) · doi:10.1145/2512530.2512534 · Cited by 27

Emotion recognition in the wild via sparse transductive transfer linear discriminant analysis

Zong, Y., Zheng, W., Huang, X., Yan, K., Yan, J., & Zhang, T. (2016) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-015-0210-7 · Cited by 27

Speech Emotion Recognition Using Semi-supervised Learning with Ladder Networks

Huang, J., Li, Y., Tao, J., Lian, Z., Niu, M., & Yi, J. (2018) · Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) · doi:10.1109/aciiasia.2018.8470363 · Cited by 27

Multimodal Deception Detection Using Automatically Extracted Acoustic, Visual, and Lexical Features

Zhang, J., Levitan, S. I., & Hirschberg, J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2320 · Cited by 27

Cross-Corpus Speech Emotion Recognition Using Joint Distribution Adaptive Regression

Zhang, J., Jiang, L., Zong, Y., Zheng, W., & Zhao, L. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414372 · Cited by 27

Speech Emotion Recognition by Late Fusion for Bidirectional Reservoir Computing With Random Projection

Ibrahim, H., Loo, C. K., & Alnajjar, F. S. K. (2021) · IEEE Access · doi:10.1109/access.2021.3107858 · Cited by 27

Speaker state recognition using an HMM-based feature extraction method

Gajšek, R., Mihelič, F., & Dobrišek, S. (2012) · Computer Speech & Language · doi:10.1016/j.csl.2012.01.007 · Cited by 27

Speaker state classification based on fusion of asymmetric simple partial least squares (SIMPLS) and support vector machines

Huang, D., Zhang, Z., & Ge, S. S. (2013) · Computer Speech & Language · doi:10.1016/j.csl.2013.06.002 · Cited by 27

A multi-modal approach to emotion recognition using undirected topic models

Shah, M., Chakrabarti, C., & Spanias, A. (2014) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2014.6865245 · Cited by 27

Contemporary Methods for Speech Parameterization

Ganchev, T. D. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8447-0 · Cited by 27

Deep Learning for Emotional Speech Recognition

Sánchez-Gutiérrez, M. E., Albornoz, E. M., Martínez-Licona, F. M., Rufiner, H. L., & Goddard, J. C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07491-7_32 · Cited by 27

Adapting to Multiple Affective States in Spoken Dialogue

Forbes-Riley, K., & Litman, D. J. (2012) · D-Scholarship@Pitt (University of Pittsburgh) · Cited by 27

The University of Passau Open Emotion Recognition System for the Multimodal Emotion Challenge

Deng, J., Cummins, N., Han, J., Xu, X., Ren, Z. X., Pandit, V., Zhang, Z., & Schuller, B. W. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3005-5_54 · Cited by 26

Transfer Subspace Learning for Unsupervised Cross-Corpus Speech Emotion Recognition

Liu, N., Zhang, B., Liu, B., Shi, J., Yang, L., Li, Z., & Zhu, J. (2021) · IEEE Access · doi:10.1109/access.2021.3094355 · Cited by 26

Multiple windowed spectral features for emotion recognition

Attabi, Y., Alam, J., Dumouchel, P., Kenny, P. J., & O’Shaughnessy, D. D. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639126 · Cited by 26

Dynamic Active Learning Based on Agreement and Applied to Emotion Recognition in Spoken Interactions

Zhang, Y., Coutinho, E., Zhang, Z., Quan, C., & Schuller, B. W. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2820774 · Cited by 26

Modelling Affect for Horror Soundscapes

Lopes, P., Liapis, A., & Yannakakis, G. N. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2695460 · Cited by 26

Learning Transferable Features for Speech Emotion Recognition

Marczewski, A., Veloso, A., & Ziviani, N. (2017) · Thematic Workshops of ACM Multimedia · doi:10.1145/3126686.3126735 · Cited by 26

Bimodal Speech Emotion Recognition Using Pre-Trained Language ModelsPreprint

Heußer, V., Freymuth, N., Constantin, S., & Waibel, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.02610 · Cited by 30

Learning and Knowledge-Based Sentiment Analysis in Movie Review Key Excerpts

Schuller, B. W., & Knaup, T. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-18184-9_39 · Cited by 25

Feature space dimension reduction in speech emotion recognition using support vector machine

Chiou, B.-C., & Chen, C.-P. (2013) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2013.6694251 · Cited by 25

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