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

Speech emotion recognition using kernel sparse representation based classifier

Sharma, P., Abrol, V., Sachdev, A., & Dileep, A. D. (2016) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2016.7760273 · Cited by 10

Trustability-Based Dynamic Active Learning for Crowdsourced Labelling of Emotional Audio Data

Hantke, S., Abstreiter, A., Cummins, N., & Schuller, B. W. (2018) · IEEE Access · doi:10.1109/access.2018.2858931 · Cited by 10

Machine Learning Based Emotion Recognition using Speech Signal

Kumar, A., & Iqbal, D. J. L. M. (2019) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.a1068.1291s52019 · Cited by 10

On Laughter and Speech-Laugh, Based on Observations of Child-Robot InteractionPreprint

Batliner, A. M., Steidl, S., Eyben, F., & Schuller, B. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11593 · Cited by 10

A Siamese Neural Network with Modified Distance Loss For Transfer Learning in Speech Emotion RecognitionPreprint

Feng, K., & Chaspari, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.03001 · Cited by 10

Interpretable Parkinson’s Disease Detection Using Group-Wise Scaling

Momeni, N., Whitling, S., & Jakobsson, A. (2025) · IEEE Access · doi:10.1109/access.2025.3540600 · Cited by 12

Emotion and Its Triggers in Human Spoken Dialogue: Recognition and Analysis

Lubis, N., Sakti, S., Neubig, G., Toda, T., Purwarianti, A., & Nakamura, S. (2016) · Signals and communication technology · doi:10.1007/978-3-319-21834-2_10 · Cited by 9

Phoneme Embedding and its Application to Speech Driven Talking Avatar Synthesis

Li, X., Wu, Z., Meng, H. M. L., Jia, J., Lou, X., & Cai, L. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-363 · Cited by 9

Prediction of Deception and Sincerity from Speech Using Automatic Phone Recognition-Based Features

Herms, R. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-971 · Cited by 9

Predicting Humor by Learning from Time-Aligned Comments

Yang, Z., Hu, B., & Hirschberg, J. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-3113 · Cited by 9

Older adults’ emotion recognition: No auditory-visual benefit for less clear expressions

Simonetti, S., Davis, C., & Kim, J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0279822 · Cited by 9

Automated Scoring of Asynchronous Interview Videos Based on Multi-Modal Window-Consistency Fusion

Lv, J., Chen, C., & Liang, Z. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3294335 · Cited by 9

Data-driven generation of text balloons based on linguistic and acoustic features of a comics-anime corpus

Matsumiya, S., Sakti, S., Neubig, G., Toda, T., & Nakamura, S. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-410 · Cited by 9

Learning speech emotion features by joint disentangling-discrimination

Xue, W., Huang, Z.-W., Luo, X., & Mao, Q. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344598 · Cited by 9

Automatic Speech Emotion and Speaker Recognition Based on Hybrid GMM and FFBNN

Sirisha Devi, J., Srinivas, Y., & Nandyala, S. (2014) · International Journal on Computational Science & Applications · doi:10.5121/ijcsa.2014.4104 · Cited by 9

Active learning for dimensional speech emotion recognition

Han, W., Li, H., Ruan, H., Ma, L., Jiayin, S., & Schuller, B. W. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-247 · Cited by 9

Speech in Minimal Invasive Surgery - Towards an Affective Language Resource of Real-life Medical Operations

Schuller, B. W., Eyben, F., Can, S., & Feußner, H. (2010) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 9

Speech Emotion Recognition Considering Local Dynamic Features

Guan, H., Liu, Z., Wang, L., Dang, J., & Yu, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00126-1_2 · Cited by 9

Emotional Speech Corpus for Persuasive Dialogue System.

Asai, S., Yoshino, K., Shinagawa, S., Sakti, S., & Nakamura, S. (2020) · Language Resources and Evaluation · Cited by 9

Acoustic and para-verbal indicators of persuasiveness in social multimedia

Shim, H. S., Park, S., Chatterjee, M., Scherer, S. J., Sagae, K., & Morency, L. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178369 · Cited by 9

Robust emotional speech classification in the presence of babble noise

Karimi, S., & Sedaaghi, M. H. (2012) · International Journal of Speech Technology · doi:10.1007/s10772-012-9176-y · Cited by 9

Emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information and semantic labels (Extended abstract)

Wu, C., & Liang, W.-B. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344613 · Cited by 9

Low level descriptors based DBLSTM bottleneck feature for speech driven talking avatar

Lan, X., Xu, L., Ning, Y., Wu, Z., Meng, H. M. L., Jia, J., & Cai, L. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472739 · Cited by 9

Speech-based non-prototypical affect recognition for child-robot interaction in reverberated environments

Wöllmer, M., Weninger, F. J., Steidl, S., Batliner, A. M., & Schuller, B. W. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-779 · Cited by 9

SEGMENTED–MEMORY RECURRENT NEURAL NETWORKS VERSUS HIDDEN MARKOV MODELS IN EMOTION RECOGNITION FROM SPEECH

Glüge, S., Böck, R., & Wendemuth, A. (2011) · International Conference on Neural Computation Theory and Applications · doi:10.5220/0003644003080315 · Cited by 9

Seeking the SuperStar: Automatic assessment of perceived singing quality

Bohm, J., Eyben, F., Schmitt, M., Harald Kosch, A., & Schuller, B. W. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966037 · Cited by 9

Teaching Machines to Know Your Depressive State: On Physical Activity in Health and Major Depressive Disorder

Qian, K., Kuromiya, H., Zhang, Z., Kim, J., Nakamura, T., Yoshiuchi, K., Schuller, B. W., & Yamamoto, Y. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857838 · Cited by 9

Automatic Detection of Major Depressive Disorder via a Bag-of-Behaviour-Words Approach

Qian, K., Kuromiya, H., Ren, Z. X., Schmitt, M., Zhang, Z., Nakamura, T., Yoshiuchi, K., Schuller, B. W., & Yamamoto, Y. (2019) · International Symposium on Image Computing and Digital Medicine (ISICDM) · doi:10.1145/3364836.3364851 · Cited by 9

Emotion recognition from speech using extended feature selection and a simple classifier

Hassan, A., & Damper, R. I. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-587 · Cited by 9

Emotion recognition in Arabic speech

Klaylat, S., Hamandi, L., Osman, Z. A., & Zantout, R. (2017) · Sensors Networks Smart and Emerging Technologies (SENSET) · doi:10.1109/senset.2017.8125028 · Cited by 9

The development of the Athens Emotional States Inventory (AESI): collection, validation and automatic processing of emotionally loaded sentences

Chaspari, T., Soldatos, C., & Maragos, P. (2015) · The World Journal of Biological Psychiatry · doi:10.3109/15622975.2015.1012228 · Cited by 8

Beyond Empirical Windowing: An Attention-Based Approach for Trust Prediction In Autonomous Vehicles

Niu, M., Zheng, Z., Akash, K., & Misu, T. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446116 · Cited by 8

A novel convolution neural network architecture with fully connected network for efficient speech emotion recognition system

Singh, V., & Prasad, S. (2025) · International Journal of Information Technology · doi:10.1007/s41870-025-02449-5 · Cited by 8

Speaker-independent negative emotion recognition

Kotti, M., Paternò, F., & Kotropoulos, C. L. (2010) · International Workshop on Cognitive Information Processing (CIP) · doi:10.1109/cip.2010.5604091 · Cited by 8

Transformer-Based Physiological Feature Learning for Multimodal Analysis of Self-Reported Sentiment

Katada, S., Okada, S., & Komatani, K. (2022) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3536221.3556576 · Cited by 8

EXTERNAL VS. INTERNAL SVM-RFE: THE SVM-RFE METHOD REVISITED AND APPLIED TO EMOTION RECOGNITION

Daassi-Gnaba, H., & Oussar, Y. (2015) · Neural Network World · doi:10.14311/nnw.2015.25.004 · Cited by 8

Speaker impact on audience comprehension for academic presentations

Curtis, K. M., Jones, G. J. F., & Campbell, N. (2016) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2993148.2993194 · Cited by 8

Spontaneous speech emotion recognition using prior knowledge

Chakraborty, R., Pandharipande, M. A., & Kopparapu, S. K. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900071 · Cited by 8

Cross-corpus Arabic and English emotion recognition

Meftah, A. H., Seddiq, Y., Alotaibi, Y. A., & Selouani, S. (2017) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2017.8388672 · Cited by 8

Detecting Concealed Information in Text and Speech

Hu, S. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1039 · Cited by 8

Exploring Transfer Learning between Scripted and Spontaneous Speech for Emotion Recognition

Li, Q., & Chaspari, T. (2019) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3340555.3353762 · Cited by 8

A Novel Hybrid Network Model Based on Attentional Multi-Feature Fusion for Deception Detection

Fang, Y., Fu, H., Tao, H., Liang, R., & Li, Z. (2020) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2020eal2051 · Cited by 8

Thin Slices of Depression: Improving Depression Detection Performance Through Data Segmentation

Alsarrani, R., Esposito, A. A., & Vinciarelli, A. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746426 · Cited by 8

A Review of Personality in Voice-Based Man Machine Interaction

Metze, F., Black, A. W., & Polzehl, T. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21605-3_40 · Cited by 8

Augmenting Affect from Speech with Generative Music

Hagerer, G., Lux, M., Ehrlich, S. K., & Cheng, G. (2015) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2702613.2732792 · Cited by 8

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

Acoustic Features and Modelling

Eyben, F. (2015) · Springer theses · doi:10.1007/978-3-319-27299-3_2 · Cited by 8

Convolutional Neural Network with Spectrogram and Perceptual Features for Speech Emotion Recognition

Zhang, L., Wang, L., Dang, J., Guo, L., & Guan, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04212-7_6 · Cited by 8

Improving speech emotion recognition based on acoustic words emotion dictionary

Wang, W., Cao, X., Li, H., Shen, L., Feng, Y., & Watters, P. (2020) · Natural Language Engineering · doi:10.1017/s1351324920000339 · Cited by 9

Multiple users' emotion recognition: Improving performance by joint modeling of affective reactions

Chanel, G., Avry, S., Molinari, G., Bétrancourt, M., & Pun, T. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273584 · Cited by 8

Audio Visual Recognition of Spontaneous Emotions In-the-Wild

Xia, X., Guo, L., Jiang, D., Pei, E., Yang, L., & Hichem Sahli, B. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3005-5_57 · Cited by 7

Audio-Based Detection of Anxiety and Depression via Vocal Biomarkers

Brueckner, R., Kwon, N., Subramanian, V., Blaylock, N., & O’Connell, H. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-53960-2_9 · Cited by 7

Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

Zhao, Y. D., Zong, Y., Wang, J., Lian, H., Lu, C., Zhao, L., & Zheng, W. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3362690 · Cited by 7

ParaLBench: A Large-Scale Benchmark for Computational Paralinguistics Over Acoustic Foundation Models

Zhang, Z., Xu, W., Dong, Z., Wang, K., Wu, Y., Peng, J., Wang, R., & Huang, D. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3506554 · Cited by 8

Children’s Emotion Recognition from Spontaneous Speech Using a Reduced Set of Acoustic and Linguistic Features

Planet, S., & Iriondo, I. (2012) · Cognitive Computation · doi:10.1007/s12559-012-9174-z · Cited by 7

Comparative Study on Feature Selection and Fusion Schemes for Emotion Recognition from Speech

Planet, S., & Iriondo, I. (2012) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2012.166 · Cited by 7

Deep Neural Networks for Shimmer Approximation in Synthesized Audio Signal

Garcia, M., & Destéfanis, E. A. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-75214-3_1 · Cited by 7

A Dementia Classification Based on Speech Analysis of Casual Talk During a Clinical Interview

Hanai, S., Kato, S., Sakuma, T., Ohdake, R., Masuda, M., & Watanabe, H. (2022) · IEEE Global Conference on Life Sciences and Technologies (LifeTech) · doi:10.1109/lifetech53646.2022.9754933 · Cited by 7

Arabic English Speech Emotion Recognition System

Seknedy, M. E., & Fawzi, S. A. (2023) · Learning and Technology Conference (L&T) · doi:10.1109/lt58159.2023.10092295 · Cited by 7

Explainable human-centered traits from head motion and facial expression dynamics

Madan, S., Gahalawat, M., Guha, T., Goecke, R., & Subramanian, R. (2025) · PLoS ONE · doi:10.1371/journal.pone.0313883 · Cited by 7

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

RESONATE: reverberation environment simulation for improved classification of speech models

Dickerson, R. F., Hoque, E., Asare, P., Nirjon, S., & Stankovic, J. A. (2014) · doi:10.5555/2602339.2602352 · Cited by 7

A Cross Gender and Cross Lingual Study on Acoustic Features for Stress Recognition in Speech.

Zuo, X., & Fung, P. (2011) · Cited by 7

Recognizing Emotionally Coloured Dialogue Speech Using Speaker-Adapted DNN-CNN Bottleneck Features

Mukaihara, K., Sakti, S., & Nakamura, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66429-3_63 · Cited by 7

Continuous Assessment of Children’s Emotional States Using Acoustic Analysis

Gong, Y., & Poellabauer, C. (2017) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2017.53 · Cited by 7

Toward RNN Based Micro Non-verbal Behavior Generation for Virtual Listener Agents

Huang, H., Fukuda, M., & Nishida, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21902-4_5 · Cited by 7

DSTL: Solution to Limitation of Small Corpus in Speech Emotion Recognition

Chen, Y., Xiao, Z., Zhang, X., & Tao, Z. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11729 · Cited by 7

PAD 3-D Speech Emotion Recognition Based on Feature Fusion

Pengfei, X., Houpan, Z., & Zhou, W. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1616/1/012106 · Cited by 7

Speech Emotion Recognition Based on BLSTM and CNN Feature Fusion

Huilian, L., Hu, W., & Wang, Y. (2020) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1145/3408127.3408192 · Cited by 7

A Semi-Supervised Speech Deception Detection Algorithm Combining Acoustic Statistical Features and Time-Frequency Two-Dimensional Features

Fu, H., Yu, H., Wang, X., Lu, X., & Zhu, C. (2023) · Brain Sciences · doi:10.3390/brainsci13050725 · Cited by 7

Sibilant Consonants Classification with Deep Neural Networks

Anjos, I., Marques, N. C., Grilo, M., Guimarães, I., Magalhães, J., & Cavaco, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30244-3_36 · Cited by 7

A Survey on the Semi Supervised Learning Paradigm in the Context of Speech Emotion Recognition

Andrade, G., Rodrigues, M., & Nováis, P. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82196-8_57 · Cited by 7

Automatic classification of speaker characteristics

Nguyen, P., Tran, D., Huang, X., & Sharma, D. K. (2010) · International Conference on Communications and Electronics (ICCE) · doi:10.1109/icce.2010.5670700 · Cited by 7

Towards Domain-Specific Cross-Corpus Speech Emotion Recognition Approach

Zhao, Y. D., Zong, Y., Lian, H., Lu, C., Shi, J., & Zheng, W. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3483964 · Cited by 6

Review of Automatic Estimation of Emotions in Speech

O’Shaughnessy, D. D. (2025) · Applied Sciences · doi:10.3390/app15105731 · Cited by 6

A Comprehensive Study on the Effectiveness of ASR Representations for Noise-Robust Speech Emotion Recognition

Shi, X., He, J., Li, X., & Toda, T. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3654273 · Cited by 6

Exploring latent structure in expressive speech

Raptis, S. (2013) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2013.6719197 · Cited by 6

Голос и психологические свойства человека: обзор современных исследований

Семеновна, Р. А., Valueva, E. А., & Panfilova, A. (2022) · Психология Журнал Высшей школы экономики · doi:10.17323/1813-8918-2022-1-195-215 · Cited by 6

Adapting Multiple Distributions for Bridging Emotions from Different Speech Corpora

Zong, Y., Lian, H., Chang, H., Lu, C., & Tang, C. (2022) · Entropy · doi:10.3390/e24091250 · Cited by 8

Enhancing Speech Emotion Recognition for Real-World Applications via ASR Integration

Li, Y. (2023) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw59127.2023.10388136 · Cited by 6

Children Age and Gender Classification Based on Speech Using ConvNets

Pérez-Espinosa, H., Ávila-George, H., Martı́nez-Miranda, J., Espinosa‐Curiel, I. E., Rodríguez‐Jacobo, J., & Cruz-Mendoza, H. A. (2018) · Research in Computing Science · doi:10.13053/rcs-147-4-2 · Cited by 6

Rude-Words Detection for Indonesian Speech Using Support Vector Machine

Novitasari, S., Lestari, D. P., Sakti, S., & Purwarianti, A. (2018) · International Conference on Asian Language Processing · doi:10.1109/ialp.2018.8629145 · Cited by 7

Deception Detection and Analysis in Spoken Dialogues based on FastText

Hosomi, N., Sakti, S., Yoshino, K., & Nakamura, S. (2018) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659614 · Cited by 6

Emotion Recognition from Speech: An Unsupervised Learning Approach

Stefano Rovetta, A., Mnasri, Z., Masulli, F., & Cabri, A. (2020) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.d.201019.002 · Cited by 6

Emulating human cognitive approach for speech emotion using MLP and GenSofNN

Kamaruddin, N., & Wahab, A. (2013) · International Conference on Information and Communication Technology for the Muslim World (ICT4M) · doi:10.1109/ict4m.2013.6518885 · Cited by 6

Multiple Instance Learning for Behavioral Coding

Gibson, J., Katsamanis, A., Romero, F., Xiao, B., Georgiou, P., & Narayanan, S. S. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2510625 · Cited by 6

Improved Speech Emotion Classification from Spectral Coefficient Optimization

Idris, I., & Salam, M. S. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-3-319-32213-1_22 · Cited by 6

Improved speech emotion recognition using error correcting codes

Chakraborty, R., & Kopparapu, S. K. (2016) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2016.7574707 · Cited by 6

Empirical evaluation of emotion classification accuracy for non-acted speech

Deshpande, G., Viraraghavan, V. S., Duggirala, M., Reddy, V. R., & Patel, S. (2017) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2017.8122261 · Cited by 6

Coupled Unsupervised Deep Convolutional Domain Adaptation for Speech Emotion Recognition

Noi, O. E. N., Qirong, M., Xu, G., & Xue, Y. (2018) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2018.8499077 · Cited by 6

Robust Speech Emotion Recognition Under Different Encoding Conditions

Oates, C., Triantafyllopoulos, A., Steiner, I., & Schuller, B. W. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1658 · Cited by 6

MEMOS: A Multi-modal Emotion Stream Database for Temporal Spontaneous Emotional State Detection

Li, Y., Xia, X., Jiang, D., Hichem Sahli, B., & Jain, R. (2020) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3395035.3425358 · Cited by 6

Prediction of Public Trust in Politicians Using a Multimodal Fusion Approach

Syed, M. S. S., Pirogova, E., & Lech, M. (2021) · Electronics · doi:10.3390/electronics10111259 · Cited by 6

Two-level discriminative speech emotion recognition model with wave field dynamics: A personalized speech emotion recognition method

Jia, N., & Zheng, C. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.09.013 · Cited by 6

Detection of anger emotion in dialog speech using prosody feature and temporal relation of utterances

Nomoto, N., Masataki, H., Yoshioka, O., & Takahashi, S. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-206 · Cited by 6

Emotion recognition from speech based on ML algorithms applied on two Serbian datasets

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