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

A Comparative Study of Emotion Recognition Models based on STT-Transcribed Text

Kim, K., Kim, Y., & Kim, J. (2025) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2025.23.9.33

Voice source characterization for prosodic and spectral manipulation

Mayos, J. P. (2012) · doi:10.5821/dissertation-2117-94722

Speech Emotion Recognition Based on Simultaneously Fusing and Transferring of Multilevel Speech Features

Zha, C., & Tang, Y. (2025) · Journal of Electrical and Computer Engineering · doi:10.1155/jece/5899808

Multilingual Speech Emotion Recognition Using Code Switched Tamil-English Data

S., H. V., Kousalya, S., & Nithya, A. (2025) · International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) · doi:10.1109/iceconf65644.2025.11379680

Analysis of Speech Features in Identifying Client's Change Talk in Motivational Interviewing

Kalluri, S. B., & Vijayasenan, D. (2025) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon66050.2025.11375239

Performance-weighted ensemble learning for detecting patients with FTD and ALS from short reading tasksPreprint

Ito, Y., Ohdake, R., Kato, S., Masuda, M., Suzuki, M., Watanabe, H., Sakuma, T., Ishibashi, K., Ogura, A., Hara, K., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-7917087/v1

Valence-Arousal Emotion Recognition Using a Deep Three-Layer Model with Aural Perceptual Representations

Li, X., & Li, J. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401009

Advanced feature selection and temporal attention mechanisms with Bi-LSTM classifier for optimizing emotion recognition in Kashmiri speech

Dar, G. M., & Delhibabu, R. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1768701

Détection multimodale du stress pour la conception de logiciels de remédiation

Soury, M. (2014) · doi:10.70675/899de45fzf2f9z4ce8z9928z16f507a53af6

Analyse d'opinion dans les interactions orales

Barriere, V. (2019) · theses.fr (ABES) · doi:10.70675/d65df106z9fe1z44f0zb9bbzb1caa1590c7a

Social Emotion Recognition with multimodal deep learning architecture in emergency call centers

Deschamps-Berger, T. (2024) · theses.fr (ABES) · doi:10.70675/e79c9094z72f1z479dzbf7dza45ee7d0c3bb

Bimodal Emotion Recognition Using Wav2Vec 2.0 for Speech and RoBERTa for Text with Late Fusion Strategy

Trinisha, G., Reddy, C. V., Swamy, B. K., Sri, K. L., & Manivardhan, M. (2026) · International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) · doi:10.1109/aimla67915.2026.11522402

DyGIN: Modality-Unified Dynamic Graph Inception Network for Context-Aware Emotion Recognition

Gao, Y., Zhao, H., & Zhang, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3701693

A Comprehensive Review of Lightweight Audio-Visual Emotion Recognition: Architectures, Fusion Techniques, and the Efficiency Trade-off

Shukla, V., Singh, M., & Sheenam (2026) · International Conference on Connected Intelligence for Industrial Applications (CI2A) · doi:10.1109/ci2a69097.2026.11576986

MindCare - AI Based Mental Wellness Voice Based Companion

Tirkhunde, R., Patole, V., Yamgar, S., Thombare, P. N., & Shinde, N. (2026) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2026.150600035

Emotion Recognition in Agile Software Meetings: A Comparative Study of ML, DL, and Text-based LLM Approaches

Canuto, T., Sardenberg, E., Mann, P., Utino, M. Y. R., COUTINHO, D. J. B., Uchôa, A., & Pereira, J. A. (2026) · International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE) · doi:10.1145/3794860.3794910

Speech Emotion Recognition with Dual-Stream Channel Attention

Kanan, Agarwal, D., & Goel, N. (2026) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms69701.2026.11602152

Sound-Mind: Enhancing Paralinguistic Understanding in MLLMs via Iterative Latent Refinement

Han, Z., Yang, X. Y., Chen, W., Wang, Z., Liu, J., Xu, T., Xing, Z., & Zhang, Z. Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3417-2_6

Automatic coding of expressed negativity from maternal speech to predict adolescent psychopathology: development and initial evaluation (Preprint)Preprint

Firth, Z., Mirheidari, B., Hussain, T., Fisher, H. L., Arseneault, L., Caspi, A., Moffitt, T. E., Wickersham, A., Downs, J. M., & Cummins, N. (2026) · doi:10.2196/preprints.106740

AI-Based Interview Preparation System using Multimodal Analysis

Simhadri, C. S. R., Jonnada, N., Chitturi, S., Guduri, J., & Gudivada, H. V. S. (2026) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon69332.2026.11629790

A Biometric Voice Security System Enhanced with Emotional State Recognition

Bhel, R., Chaudhary, H., Tyagi, A., Saha, S., & Jakimoski, K. (2026) · International Conference on Educational Technology Management (ICETM) · doi:10.1109/icetm68138.2026.11648091

ReflectAI: An AI-Driven Multimodal Journaling App for Promoting Mental Well-being

Kurian, J., Benjamin, A. R., Irudayaraj, G., Orji, R., & Oyebode, O. (2026) · SeGAH ... IEEE ... International Conference on Serious Games and Applications for Health/IEEE International Conference on Serious Games and Applications for Health · doi:10.1109/segah69272.2026.11653358

A Multimodal Deep Learning Framework for Depression Detection Using Face, Voice, and Questionnaire

Noorie, O., Chakraborty, A., & Mukhopadhyay, M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-28206-4_17

Speech–Text Fusion With ASR Reliability Metadata for Speech Emotion Recognition

Wan, K. (2026) · IEEE Access · doi:10.1109/access.2026.3737228

Continuous Ordinal Representation of Neural Irritation in IONM via Geometric Modeling

Aussavavirojekul, P., Sclabassi, R. J., Yu, A. K.-T., Wehbe, L., & Riviere, C. N. (2026) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-032-37736-4_28

Cross-Modal Attention and Tensor Fusion for Deep Audio-Visual Emotion Detection

Chowdhury, A. K., & Penmethsa, J. (2026) · International Conference on Smart Communication and Sustainable Technologies (ICSCST) · doi:10.1109/icscst70031.2026.11709780

Binaural Speech and Distribution Learning for Predicting Auditory Distance Perception in Audio Augmented Reality

Altalhi, S., Guha, T., & Vinciarelli, A. (2026) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3776574.3831162

Speech Emotion Recognition Beyond Benchmarks: Robustness, Efficiency, and Evaluation for Real-World Deployment

Beyer, D., Mazura, F., Köder, J., & Stork, W. (2026) · International Conference on Technology Innovation and Its Applications (ICTIIA) · doi:10.1109/ictiia70925.2026.11717155

SPHERE-SER: A Streaming Psychoacoustic Asymmetric-Spread Scaler for Speech Emotion Recognition Features

Gumelar, A. B., Irzan, R. M. Y., & Lienardi, G. G. (2026) · International Conference on Technology Innovation and Its Applications (ICTIIA) · doi:10.1109/ictiia70925.2026.11717400

Differentiating non-fluent and logopenic primary progressive aphasia via automated acoustic speech analysis

Antonicelli, G., Morin, B. T., Bogley, R., Newbury, R., Ezzes, Z., Ramkrishnan, S., Miller, Z., Mandelli, M. L., Tee, B. L., García, A. M., et al. (2026) · Cortex · doi:10.1016/j.cortex.2026.10.006

Der hörbare Nutzen: Wie Audio KI aus Stimme Biomarker macht

Schuller, D. (2026) · Springer eBooks · doi:10.1007/978-3-662-73474-2_34

Multimodal Affective Game Adaptation Using Voice Spectral and Gameplay Telemetry Fusion

Bhattacharjee, P., CHANDEL, M., Malakar, K., Banakar, B. S., Das, P. M., & Liketh, B. (2026) · doi:10.1109/icsl-dsga70378.2026.11736417

Robust Speech Emotion Recognition Through Self-Supervised Acoustic-Semantic Fusion and Cross-Attention Under Background Noise

Jadhav, R. B. A. D. H. B. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-40501

Parametric Complexity vs. Domain Stability in SER: From Acted to Spontaneous Speech Under Speaker-Disjoint Controls

Jabnoun, J., & Zrigui, M. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.09.085

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