OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit

Long Short-Term Memory

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LIBSVM: A library for support vector machines

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Data Mining: Practical Machine Learning Tools and Techniques

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Libsvm : A library for support vector machines

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Praat: Doing Phonetics by Computer

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A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

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A database of German emotional speech

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The INTERSPEECH 2009 emotion challenge

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Abandoning emotion classes - towards continuous emotion recognition with modelling of long-range dependencies

Wöllmer, 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 333

Being bored? Recognising natural interest by extensive audiovisual integration for real-life application

Schuller, 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

Estimation of unknown speaker’s height from speech

Mporas, I., & Ganchev, T. D. (2009) · International Journal of Speech Technology · doi:10.1007/s10772-010-9064-2 · Cited by 48

Can prosody inform sentiment analysis? Experiments on short spoken reviews

Mairesse, F., Polifroni, J. H., & Fabbrizio, G. D. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6289066 · Cited by 46

EMODA

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Illegal Logging Detection Based on Acoustic Surveillance of Forest

Mporas, I., Perikos, I., Kelefouras, V. I., & Paraskevas, M. (2020) · Applied Sciences · doi:10.3390/app10207379 · Cited by 44

Multimodal Emotion Recognition Using Feature Fusion: An LLM-Based Approach

Chandraumakantham, O., Gowtham, N., Zakariah, M., & Almazyad, A. S. (2024) · IEEE Access · doi:10.1109/access.2024.3425953 · Cited by 43

Infinite Hidden Conditional Random Fields for Human Behavior Analysis

Bousmalis, K., Zafeiriou, S. P., Morency, L., & Pantić, M. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2224882 · Cited by 43

Classification of emotional speech using 3DEC hierarchical classifier

Hassan, A., & Damper, R. I. (2012) · Speech Communication · doi:10.1016/j.specom.2012.03.003 · Cited by 43

Identifying salient sub-utterance emotion dynamics using flexible units and estimates of affective flow

Provost, E. M. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638345 · Cited by 41

Affective speech interface in serious games for supporting therapy of mental disorders

Κωστούλας, Θ., Mporas, I., Kocsis, O., Ganchev, T. D., Katsaounos, N., Santamaria, J. J., Jiménez‐Múrcia, S., Fernández‐Aranda, F., & Fakotakis, N. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.03.067 · Cited by 42

A feature selection and feature fusion combination method for speaker-independent speech emotion recognition

Jin, Y., Song, P., Zheng, W., & Zhao, L. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854515 · Cited by 40

The social signal interpretation framework (SSI) for real time signal processing and recognition

Wagner, J., Lingenfelser, F., & André, E. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-818 · Cited by 40

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

Sentiment analysis from Customer-generated online videos on product review using topic modeling and Multi-attention BLSTM

Wang, Z., Gao, P., & Chu, X. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101588 · Cited by 40

Sentence level emotion recognition based on decisions from subsentence segments

Jeon, J. H., Xia, R., & Liu, Y. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5947464 · Cited by 38

Intoxicated speech detection by fusion of speaker normalized hierarchical features and GMM supervectors

Bone, D., Black, M. P., Li, M., Metallinou, A., Lee, S., & Narayanan, S. S. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-805 · Cited by 38

Highly Accurate Gear Fault Diagnosis Based on Support Vector Machine

Abdul, Z. K., & Al‐Talabani, A. K. (2022) · Journal of Vibration Engineering & Technologies · doi:10.1007/s42417-022-00768-6 · Cited by 38

Acoustic feature selection and classification of emotions in speech using a 3D continuous emotion model

Pérez-Espinosa, H., Reyes-Garcia, C. A., & Villaseñor-Pineda, L. (2011) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2011.02.008 · Cited by 37

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

Selecting Training Data for Cross-Corpus Speech Emotion Recognition: Prototypicality vs. Generalization

Schuller, B. W., Zhang, Z., Weninger, F. J., & Rigoll, G. (2011) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 37

SenticNet

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High-Performance Modelling and Simulation for Big Data Applications

Kołodziej, J., & González–Vélez, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16272-6 · Cited by 37

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

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

New technologies for information retrieval to achieve situational awareness and higher patient safety in the surgical operating room: the MRI institutional approach and review of the literature

Kranzfelder, M., Schneider, A., Gillen, S., & Feußner, H. (2010) · Surgical Endoscopy · doi:10.1007/s00464-010-1239-z · 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

Speech emotion recognition based on Gaussian Mixture Models and Deep Neural Networks

Tashev, I., Wang, Z.-Q., & Godin, K. W. (2017) · Information Theory and Applications Workshop (ITA) · doi:10.1109/ita.2017.8023477 · Cited by 34

Inferring Sentiments from Supervised Classification of Text and Speech cues using Fuzzy Rules

Vashishtha, S., & Susan, S. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.348 · Cited by 33

Exploring Multimodal Multiscale Features for Sentiment Analysis Using Fuzzy-Deep Neural Network Learning

Wang, X., Lyu, J., Kim, B., Parameshachari, B. D., Li, K., & Li, Q. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3419140 · Cited by 38

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

A Multimodal Dataset for Deception Detection

Pérez‐Rosas, V., Mihalcea, R. F., Narvaez, A., & Burzo, M. G. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3go39mfxnfzo · Cited by 32

Multimodal Affect Recognition: Current Approaches and Challenges

Osman, H. A., & Falk, T. H. (2017) · InTech eBooks · doi:10.5772/65683 · Cited by 37

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

Investigating Glottal Parameters and Teager Energy Operators in Emotion Recognition

Sun, R., & Moore, E. (2011) · Affective Computing and Intelligent Interaction · doi:10.1007/978-3-642-24571-8_54 · Cited by 32

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

Induction of Emotional States in Educational Video Games Through a Fuzzy Control System

Lara-Álvarez, C. A., Mitre-Hernández, H. A., Flores, J. J., & Pérez-Espinosa, H. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2840988 · Cited by 30

A Survey of Computational Approaches and Challenges in Multimodal Sentiment Analysis

Huddar, M. G., Sannakki, S. S., & Rajpurohit, V. S. (2019) · International Journal of Computer Sciences and Engineering · doi:10.26438/ijcse/v7i1.876883 · Cited by 31

Multimodal Sentiment Analysis—A Comprehensive Survey From a Fusion Methods Perspective

Zhao, K., Zheng, M., Li, Q., & Liu, J. (2025) · IEEE Access · doi:10.1109/access.2025.3554665 · Cited by 39

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

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

A review and critical analysis of multimodal datasets for emotional AI

Al-Azani, S., & El-Alfy, E.-S. M. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11271-1 · Cited by 37

A hierarchical static-dynamic framework for emotion classification

Mower, E., & Narayanan, S. S. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946960 · Cited by 28

Representation of facial expression categories in continuous arousal–valence space: Feature and correlation

Zhang, L., Tjondronegoro, D., & Chandran, V. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.09.005 · Cited by 27

Multimodal mixed emotion detection

Patwardhan, A. (2017) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/cesys.2017.8321250 · Cited by 27

Sentiment Analysis from Images of Natural Disasters

Hassan, S. Z., Ahmad, K., Al‐Fuqaha, A., & Conci, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30645-8_10 · Cited by 27

Speech Emotion Recognition Using Deep Learning on audio recordings

Suganya, S., & Charles, E. Y. A. (2019) · International Conference on Advances in ICT for Emerging Regions · doi:10.1109/icter48817.2019.9023737 · Cited by 31

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

A systematic strategy for robust automatic dialect identification

Liu, G., & Hansen, J. H. L. (2011) · Cited by 27

Within and cross-corpus speech emotion recognition using latent topic model-based features

Shah, M., Chakrabarti, C., & Spanias, A. (2015) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-014-0049-y · Cited by 27

Confidence measures in speech emotion recognition based on semi-supervised learning

Deng, J., & Schuller, B. W. (2012) · Interspeech (USB) · doi:10.21437/interspeech.2012-127 · Cited by 26

Age and gender detection in the I-DASH project

Meinedo, H., & Trancoso, I. M. (2011) · ACM Transactions on Speech and Language Processing · doi:10.1145/1998384.1998387 · Cited by 26

Combining semantic and acoustic features for valence and arousal recognition in speech

Karadogan, S. G., & Larsen, J. O. (2012) · International Workshop on Cognitive Information Processing (CIP) · doi:10.1109/cip.2012.6232924 · Cited by 26

SPeech ACoustic (SPAC): A novel tool for speech feature extraction and classification

Özseven, T., & Düğenci, M. (2018) · Applied Acoustics · doi:10.1016/j.apacoust.2018.02.009 · Cited by 26

Emotion Recognition in Naturalistic Speech and Language—A Survey

Weninger, F. J., Wöllmer, M., & Schuller, B. W. (2015) · Wiley eBooks · doi:10.1002/9781118910566.ch10 · Cited by 26

Response selection and turn-taking for a sensitive artificial listening agent

ter Maat, M. (2011) · doi:10.3990/1.9789461911056 · Cited by 26

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

Multimodal gender detection

Abouelenien, M., Pérez‐Rosas, V., Mihalcea, R. F., & Burzo, M. G. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3136770 · Cited by 25

Emotional Speaker Recognition based on Machine and Deep Learning

Sefara, T. J., & Mokgonyane, T. B. (2020) · International Multidisciplinary Information Technology and Engineering Conference (IMITEC) · doi:10.1109/imitec50163.2020.9334138 · Cited by 25

Multi-Label Speech Emotion Recognition via Inter-Class Difference Loss Under Response Residual Network

Li, X., Zhang, Z., Gan, C., & Xiang, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3157485 · Cited by 25

Unimodal approaches for emotion recognition: A systematic review

Tomar, P. S., Mathur, K., & Suman, U. (2022) · Cognitive Systems Research · doi:10.1016/j.cogsys.2022.10.012 · Cited by 25

Trends in speech emotion recognition: a comprehensive survey

Kaur, K., & Singh, P. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14656-y · Cited by 26

Jointly Aligning and Predicting Continuous Emotion Annotations

Khorram, S., McInnis, M. G., & Provost, E. M. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2917047 · Cited by 25

Audio Features Selection for Automatic Height Estimation from Speech

Ganchev, T. D., Mporas, I., & Fakotakis, N. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12842-4_12 · Cited by 24

Comparative Deep Network Analysis of Speech Emotion Recognition Models using Data Augmentation

Prasanna, Y. L., Tarakaram, Y., Mounika, Y., Palaniswamy, S., & Vekkot, S. (2022) · International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON) · doi:10.1109/centcon56610.2022.10051557 · Cited by 23

Enhancing Emotion Recognition from Speech through Feature Selection

Κωστούλας, Θ., Ganchev, T. D., Lazaridis, A., & Fakotakis, N. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15760-8_43 · Cited by 23

A Deep Feature based Multi-kernel Learning Approach for Video Emotion Recognition

Li, W., Abtahi, F., & Zhu, Z. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2830583 · Cited by 23

Synthesized speech for model training in cross-corpus recognition of human emotion

Schuller, B. W., Zhang, Z., Weninger, F. J., & Burkhardt, F. (2012) · International Journal of Speech Technology · doi:10.1007/s10772-012-9158-0 · Cited by 22

A high-precision feature extraction network of fatigue speech from air traffic controller radiotelephony based on improved deep learning

Shen, Z., & Wei, Y. (2021) · ICT Express · doi:10.1016/j.icte.2021.01.002 · Cited by 22

Incorporating Interpersonal Synchronization Features for Automatic Emotion Recognition from Visual and Audio Data during Communication

Quan, J., Miyake, Y., & Nozawa, T. (2021) · Sensors · doi:10.3390/s21165317 · Cited by 23

Affect Intensity Estimation Using Multiple ModalitiesPreprint

Patwardhan, A., & Knapp, G. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.01075 · Cited by 22

Learning and evaluating response prediction models using parallel listener consensus

de Kok, I., Özkan, D., Heylen, D., & Morency, L. (2010) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1891903.1891908 · Cited by 21

Applicability of Emotion Recognition and Induction Methods to Study the Behavior of Programmers

Wróbel, M. R. (2018) · Applied Sciences · doi:10.3390/app8030323 · Cited by 21

Revisiting the EmotiW challenge: how wild is it really?

Kächele, M., Schels, M., Meudt, S., Palm, G., & Schwenker, F. (2016) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-015-0202-7 · Cited by 21

Instrumenting and Analyzing Fabrication Activities, Users, and Expertise

Gong, J., Anderson, F., Fitzmaurice, G. W., & Grossman, T. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300554 · Cited by 21

Speech emotion recognition using data augmentation

Praseetha, V. M., & Joby, P. P. (2021) · International Journal of Speech Technology · doi:10.1007/s10772-021-09883-3 · Cited by 21

Disentangled-Multimodal Privileged Knowledge Distillation for Depression Recognition with Incomplete Multimodal Data

Pan, Y., Jiang, J., Jiang, K., & Liu, X. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681227 · Cited by 20

Multimodal Automated Fact-Checking: A Survey

Akhtar, M., Schlichtkrull, M., Guo, Z., Cocarascu, O., Simperl, E. P. B., & Vlachos, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.361 · Cited by 34

Emotion recognition with boosted tree classifiers

Day, M. (2013) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2522848.2531740 · Cited by 20

The Role of Linguistic and Prosodic Cues on the Prediction of Self-Reported Satisfaction in Contact Centre Phone Calls

Luque, J., Segura, C., Sánchez, A., Umbert, M., & Galindo, L. Á. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-424 · Cited by 20

Prosodic, Spectral and Voice Quality Feature Selection Using a Long-Term Stopping Criterion for Audio-Based Emotion Recognition

Kächele, M., Zharkov, D., Meudt, S., & Schwenker, F. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.148 · Cited by 19

On the development of an automatic voice pleasantness classification and intensity estimation system

Coelho, L. P., Braga, D., Dias, M. S., & Mateo, C. G. (2012) · Computer Speech & Language · doi:10.1016/j.csl.2012.01.006 · Cited by 19

Optimizing Speech Emotion Recognition with Deep Learning and Grey Wolf Optimization: A Multi-Dataset Approach

Tyagi, S., & Szénási, S. (2024) · Algorithms · doi:10.3390/a17030090 · Cited by 18

Multimodal Sentiment Analysis of Arabic Videos

Najadat, H., & Shaqra, F. A. (2018) · Journal of Image and Graphics · doi:10.18178/joig.6.1.39-43 · Cited by 18

Latent Mixture of Discriminative Experts for Multimodal Prediction Modeling

Özkan, D., Sagae, K., & Morency, L. (2010) · Cited by 18

EmoWisconsin: An Emotional Children Speech Database in Mexican Spanish

Pérez-Espinosa, H., Reyes-Garcia, C. A., & Villaseñor-Pineda, L. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_7 · Cited by 18

Using a PCA-based dataset similarity measure to improve cross-corpus emotion recognition

Siegert, I., Böck, R., & Wendemuth, A. (2018) · Computer Speech & Language · doi:10.1016/j.csl.2018.02.002 · Cited by 18

Exploiting IoT services by integrating emotion recognition in Web of Objects

Jarwar, M. A., & Chong, I. (2017) · International Conference on Information Networking · doi:10.1109/icoin.2017.7899474 · Cited by 17

Multimodal(Audio, Facial and Gesture) based Emotion Recognition challenge

Gou, W., Li, J., & Sun, M. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00142 · Cited by 17

AMGCN: An adaptive multi-graph convolutional network for speech emotion recognition

Lian, H., Lu, C., Chang, H., Yan, Z., Li, S., Li, Y., & Zong, Y. (2025) · Speech Communication · doi:10.1016/j.specom.2024.103184 · Cited by 17

A novel feature extraction strategy for multi-stream robust emotion identification

Liu, G., Lei, Y., & Hansen, J. H. L. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-203 · Cited by 16

Efficient speech emotion recognition using binary support vector machines & multiclass SVM

Nelapati, R. K., & Saraswathi, S. (2015) · IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) · doi:10.1109/iccic.2015.7435793 · Cited by 17

Automatic Prediction of Suicidal Risk in Military Couples Using Multimodal Interaction Cues from Couples Conversations

Chakravarthula, S. N., Nasır, Tseng, S.-Y., Li, H., Park, T., Baucom, B., Bryan, C. J., Narayanan, S. S., & Georgiou, P. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053246 · Cited by 17

An Active Data Representation of Videos for Automatic Scoring of Oral Presentation Delivery Skills and Feedback Generation

Haider, F., Koutsombogera, M., Conlan, O., Vogel, C. M., Campbell, N., & Luz, S. F. (2020) · Frontiers in Computer Science · doi:10.3389/fcomp.2020.00001 · Cited by 16

A Deep Learning System for Sentiment Analysis of Service Calls

Jia, Y. (2020) · Workshop on e-Commerce and NLP (ECNLP) · doi:10.18653/v1/2020.ecnlp-1.4 · Cited by 21

Speaker height estimation from speech: Fusing spectral regression and statistical acoustic models

Hansen, J. H. L., Williams, K. A., & Bořil, H. (2015) · The Journal of the Acoustical Society of America · doi:10.1121/1.4927554 · Cited by 16

Dimensionality reduction for speech emotion features by multiscale kernels

Xu, X., Deng, J., Zheng, W., Zhao, L., & Schuller, B. W. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-335 · Cited by 16

Automatic Speech Feature Learning for Continuous Prediction of Customer Satisfaction in Contact Center Phone Calls

Segura, C., Balcells, D., Umbert, M., Arias, J., & Luque, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49169-1_25 · Cited by 16

Real-time automatic emotion recognition from speech

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

Inhomogeneous Point-Processes to Instantaneously Assess Affective Haptic Perception through Heartbeat Dynamics Information

Valenza, G., Greco, A., Citi, L., Bianchi, M., Barbieri, R., & Scilingo, E. P. (2016) · Scientific Reports · doi:10.1038/srep28567 · Cited by 15

A novel perceptual feature set for audio emotion recognition

Sezgin, M. C., Günsel, B., & Kurt, G. K. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771348 · Cited by 15

speech-emotion: A multilingual and multimodal toolkit for emotion recognition from speech

Pan, R., Bernal-Beltrán, T., Garcí­a-Dí­az, J. A., & Valencia‐García, R. (2026) · SoftwareX · doi:10.1016/j.softx.2026.102677 · Cited by 1

Speech Emotion Recognition using Machine Learning

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