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

Bird sounds classification by large scale acoustic features and extreme learning machine

Qian, K., Zhang, Z., Ringeval, F., & Schuller, B. W. (2015) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2015.7418412 · Cited by 25

Introducing the Urdu-Sindhi Speech Emotion Corpus: A Novel Dataset of Speech Recordings for Emotion Recognition for Two Low-Resource Languages

Syed, Z. S., Ali, S., Shehram, M., & Shah, A. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.01104104 · Cited by 25

Multimodal Political Deception Detection

Kamboj, M., Hessler, C., Asnani, P., Riani, K., & Abouelenien, M. (2020) · IEEE Multimedia · doi:10.1109/mmul.2020.3048044 · Cited by 25

Speech Emotion Recognition System for Human Interaction Applications

Seknedy, M. E., & Fawzi, S. A. (2021) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/icicis52592.2021.9694246 · Cited by 25

Speaker variability in emotion recognition - an adaptation based approach

Ding, N., Sethu, V., Epps, J., & Eliathamby Ambikairajah, A. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6289068 · Cited by 25

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

Canonical correlation analysis and local fisher discriminant analysis based multi-view acoustic feature reduction for physical load prediction

Kaya, H., Özkaptan, T., Salah, A. A., & Gürgen, F. S. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-107 · Cited by 25

Enhancement of emotion detection in spoken dialogue systems by combining several information sources

López-Cózar, R., Silovský, J., & Kroul, M. (2011) · Speech Communication · doi:10.1016/j.specom.2011.01.006 · Cited by 24

Real-life emotion detection from speech in human-robot interaction: experiments across diverse corpora with child and adult voices

Tahon, M., Delaborde, A., & Devillers, L. Y. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-781 · Cited by 24

Using Fisher Vector and Bag-of-Audio-Words Representations to Identify Styrian Dialects, Sleepiness, Baby & Orca Sounds

Gosztolya, G. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1726 · Cited by 24

Cross-Corpus Speech Emotion Recognition Based on Deep Domain-Adaptive Convolutional Neural Network

Liu, J., Zheng, W., Zong, Y., Lu, C., & Tang, C. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019edl8136 · Cited by 24

A Two-Dimensional Framework of Multiple Kernel Subspace Learning for Recognizing Emotion in Speech

Xu, X., Deng, J., Cummins, N., Zhang, Z., Wu, C., Li, Z., & Schuller, B. W. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2694704 · Cited by 24

Anger Detection in Arabic Speech Dialogs

Khalil, A., Al-Khatib, W. G., El-Alfy, E.-S. M., & Cheded, L. (2018) · International Conference on Computing Sciences and Engineering (ICCSE) · doi:10.1109/iccse1.2018.8374203 · Cited by 24

Speaker to Emotion: Domain Adaptation for Speech Emotion Recognition with Residual Adapters

Xi, Y., Li, P., Song, Y., Jiang, Y., & Dai, L.-R. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023339 · Cited by 24

Incremental acoustic valence recognition: an inter-corpus perspective on features, matching, and performance in a gating paradigm

Schuller, B. W., & Devillers, L. Y. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-289 · Cited by 24

Joint low rank embedded multiple features learning for audio–visual emotion recognition

Wang, Z., Wang, L., & Huang, H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.017 · Cited by 23

ABAW5 Challenge: A Facial Affect Recognition Approach Utilizing Transformer Encoder and Audiovisual Fusion

Zhang, Z., An, L., Cui, Z., Xu, A., Dong, T., Jiang, Y., Shi, J., Liu, X., Sun, X., & Wang, M. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00607 · 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

Use of Line Spectral Frequencies for Emotion Recognition from Speech

Bozkurt, E., Erzin, E., Erdem, Ç. E., & Erdem, A. T. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.903 · Cited by 23

Speaker states recognition using latent factor analysis based Eigenchannel factor vector modeling

Li, M., Metallinou, A., Bone, D., & Narayanan, S. S. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288284 · Cited by 23

Speaker Emotion Recognition based on Speech Features and Classification Techniques

Devi, J. S., Yarramalle, S., & Nandyala, S. (2014) · International Journal of Image Graphics and Signal Processing · doi:10.5815/ijigsp.2014.07.08 · Cited by 24

IESC-Child: An Interactive Emotional Children’s Speech Corpus

Pérez-Espinosa, H., Martı́nez-Miranda, J., Espinosa‐Curiel, I. E., Rodríguez‐Jacobo, J., Villaseñor-Pineda, L., & Ávila-George, H. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.06.006 · Cited by 23

Transfer Learning for Improving Speech Emotion Classification AccuracyPreprint

Latif, S., Rana, R. K., Younis, M. S., Qadir, J., & Epps, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.06353 · Cited by 23

Cross Lingual Cross Corpus Speech Emotion RecognitionPreprint

Goel, S., & Beigi, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.07996 · Cited by 23

Learning Multimodal Representations for Drowsiness Detection

Qian, K., Koike, T., Nakamura, T., Schuller, B. W., & Yamamoto, Y. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3105326 · Cited by 22

Music Theory-Inspired Acoustic Representation for Speech Emotion Recognition

Li, X., Shi, X., Hu, D., Li, Y., Zhang, Q., Wang, Z., Unoki, M., & Akagi, M. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3289312 · Cited by 22

“you made me do it”: classification of blame in married couples' interactions by fusing automatically derived speech and language information

Black, M. P., Georgiou, P., Katsamanis, A., Baucom, B., & Narayanan, S. S. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-23 · Cited by 22

Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments

Bang, J., Hur, T., Kim, D., Huynh‐The, T., Lee, J., Han, Y., Baños, O., Kim, J., & Lee, S. (2018) · Sensors · doi:10.3390/s18113744 · Cited by 22

Dual Exclusive Attentive Transfer for Unsupervised Deep Convolutional Domain Adaptation in Speech Emotion Recognition

Ocquaye, E. N. N., Mao, Q., Song, H., Xu, G., & Xue, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2924597 · Cited by 22

Combining spectral and prosodic information for emotion recognition in the interspeech 2009 emotion challenge

Luengo, I., Navas, E., & Hernáez, I. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-108 · Cited by 22

Automatic recognition of self-reported and perceived emotion: does joint modeling help?

Zhang, B., Essl, G., & Mower Provost, E. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2993173 · Cited by 21

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

Natural Arabic Language Resources for Emotion Recognition in Algerian Dialect

Dahmani, H., Hussein, H., Meyer-Sickendiek, B., & Jokisch, O. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-32959-4_2 · Cited by 21

Co-training succeeds in Computational Paralinguistics

Zhang, Z., Deng, J., & Schuller, B. W. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639325 · Cited by 21

Towards a standard set of acoustic features for the processing of emotion in speech.

Eyben, F., Batliner, A. M., & Schuller, B. W. (2010) · Proceedings of meetings on acoustics · doi:10.1121/1.4739483 · Cited by 21

Speaker state classification based on fusion of asymmetric SIMPLS and support vector machines

Huang, D., Ge, S. S., & Zhang, Z. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-813 · Cited by 21

Active learning by label uncertainty for acoustic emotion recognition

Zhang, Z., Deng, J., Marchi, E., & Schuller, B. W. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-250 · Cited by 21

Expressive Speech Driven Talking Avatar Synthesis with DBLSTM Using Limited Amount of Emotional Bimodal Data

Li, X., Wu, Z., Meng, H. M. L., Jia, J., Lou, X., & Cai, L. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-364 · Cited by 21

Automatic classification of Furnariidae species from the Paranaense Littoral region using speech-related features and machine learning

Albornoz, E. M., Vignolo, L. D., Sarquis, J. A., & León, E. J. (2017) · Ecological Informatics · doi:10.1016/j.ecoinf.2017.01.004 · Cited by 21

An Investigation of Partition-Based and Phonetically-Aware Acoustic Features for Continuous Emotion Prediction from Speech

Huang, Z., & Epps, J. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2821135 · Cited by 21

SynPaFlex-Corpus: An Expressive French Audiobooks Corpus dedicated to expressive speech synthesis.

Sini, A., Lolive, D., Vidal, G., Tahon, M., & Delais-Roussarie, É. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2rdkz74u8h74 · Cited by 23

Analysis of acoustic and voice quality features for the classification of infant and mother vocalizations

Li, J., Hasegawa‐Johnson, M., & McElwain, N. L. (2021) · Speech Communication · doi:10.1016/j.specom.2021.07.010 · Cited by 21

Processing affected speech within human machine interaction

Vlasenko, B., & Wendemuth, A. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-586 · Cited by 21

Voice source features for cognitive load classification

Yap, T. F., Epps, J., Eliathamby Ambikairajah, A., & Choi, E. H. C. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5947654 · Cited by 21

Video emotion recognition in the wild based on fusion of multimodal features

Chen, S., Li, X., Jin, Q., Zhang, S., & Qin, Y. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997629 · Cited by 20

Speech Emotion Recognition Based on Linear Discriminant Analysis and Support Vector Machine Decision Tree

Mao, J.-W., He, Y., & Liu, Z. (2018) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2018.8482931 · Cited by 20

Paralinguistic singing attribute recognition using supervised machine learning for describing the classical tenor solo singing voice in vocal pedagogy

Xu, Y., Wang, W., Cui, H., Xu, M., & Li, M. (2022) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-022-00240-z · Cited by 20

Distributing Recognition in Computational Paralinguistics

Zhang, Z., Coutinho, E., Deng, J., & Schuller, B. W. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2359655 · Cited by 20

Investigation of Speaker Group-Dependent Modelling for Recognition of Affective States from Speech

Siegert, I., Philippou-Hübner, D., Hartmann, K., Böck, R., & Wendemuth, A. (2014) · Cognitive Computation · doi:10.1007/s12559-014-9296-6 · Cited by 20

Fusion of Acoustic and Linguistic Features for Emotion Detection

Metze, F., Polzehl, T., & Wagner, M. (2009) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc.2009.32 · Cited by 20

Similarity Search of Acted Voices for Automatic Voice Casting

Obin, N., & Röebel, A. (2016) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2016.2580302 · Cited by 23

Speech Emotion Recognition using Convolutional Neural Network with Audio Word-based Embedding

Huang, K.-Y., Wu, C., Hong, Q.-B., Su, M.-H., & Zeng, Y.-R. (2018) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp.2018.8706610 · Cited by 20

Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

Dinkov, Y., Ali, A. M. A., Koychev, I. K., & Nakov, P. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2965 · Cited by 20

Using the Fisher Vector Representation for Audio-based Emotion Recognition

Gosztolya, G. (2020) · Acta Polytechnica Hungarica · doi:10.12700/aph.17.6.2020.6.1 · Cited by 20

“Are You Playing a Shooter Again?!” Deep Representation Learning for Audio-Based Video Game Genre Recognition

Amiriparian, S., Cummins, N., Gerczuk, M., Pugachevskiy, S., Ottl, S., & Schuller, B. W. (2019) · IEEE Transactions on Games · doi:10.1109/tg.2019.2894532 · Cited by 21

Deep Implicit Distribution Alignment Networks for cross-Corpus Speech Emotion Recognition

Zhao, Y., Wang, J., Zong, Y., Zheng, W., Lian, H., & Zhao, L. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10095388 · Cited by 19

Speech Emotion Recognition in Mental Health: Systematic Review of Voice-Based Applications

Jordan, E., Terrisse, R., Lucarini, V., Alrahabi, M., Krebs, M., Desclés, J., & Lemey, C. (2025) · JMIR Mental Health · doi:10.2196/74260 · Cited by 19

Speech Paralinguistic Approach for Detecting Dementia Using Gated Convolutional Neural Network

Makiuchi, M. R., Warnita, T., Inoue, N., Shinoda, K., Yoshimura, M., Kitazawa, M., Funaki, K., Eguchi, Y., & Kishimoto, T. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2020edp7196 · Cited by 19

Bidirectional parallel echo state network for speech emotion recognition

Ibrahim, H., Loo, C. K., & Alnajjar, F. S. K. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07410-2 · Cited by 19

Automatic vocalisation-based detection of fragile X syndrome and Rett syndrome

Pokorny, F. B., Schmitt, M., Egger, M., Bartl-Pokorny, K. D., Zhang, D., Schuller, B. W., & Marschik, P. B. (2022) · Scientific Reports · doi:10.1038/s41598-022-17203-1 · Cited by 19

Self-labeling with feature transfer for speech emotion recognition

Wen, G., Liao, H., Li, H., Wen, P., Zhang, T., Sande, G. A. O., & Wang, B. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109589 · Cited by 19

SPEAKER VGG CCT

Arezzo, A., & Berretti, S. (2022) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3551626.3564937 · Cited by 19

A Feature Fusion Model with Data Augmentation for Speech Emotion Recognition

Tu, Z., Liu, B., Zhao, W., Yan, R., & Zou, Y. (2023) · Applied Sciences · doi:10.3390/app13074124 · Cited by 20

Can we detect speakers' empathy?: A real-life case study

Alam, F., Danieli, M., & Riccardi, G. (2016) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2016.7804525 · Cited by 19

Learning Spontaneity to Improve Emotion Recognition in Speech

Mangalam, K., & Guha, T. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1872 · Cited by 19

Modeling Short-Term and Long-Term Dependencies of the Speech Signal for Paralinguistic Emotion Classification

Verkholyak, O., Kaya, H., & Karpov, A. A. (2019) · Informatics and Automation · doi:10.15622/sp.18.1.30-56 · Cited by 19

Exploring the benefits of discretization of acoustic features for speech emotion recognition

Vogt, T., & André, E. (2009) · Interspeech 2009 · doi:10.21437/interspeech.2009-107 · Cited by 19

Emotion recognition using semi-supervised feature selection with speaker normalization

Sun, Y., & Wen, G. (2015) · International Journal of Speech Technology · doi:10.1007/s10772-015-9272-x · Cited by 19

Getting the subtext without the text: Scalable multimodal sentiment classification from visual and acoustic modalities

Blanchard, N., Moreira, D., Bharati, A., & Scheirer, W. J. (2018) · doi:10.18653/v1/w18-3301 · Cited by 18

Exploiting Smartphone Voice Recording as a Digital Biomarker for Parkinson’s Disease Diagnosis

He, T., Chen, J., Xu, X., & Wang, W. W. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3391339 · Cited by 18

Applications of AI-Enabled Deception Detection Using Video, Audio, and Physiological Data: A Systematic Review

King, S. L., & Neal, T. J. (2024) · IEEE Access · doi:10.1109/access.2024.3462825 · Cited by 22

Exploration of an Independent Training Framework for Speech Emotion Recognition

Zhong, S., Yu, B., & Zhang, H. (2020) · IEEE Access · doi:10.1109/access.2020.3043894 · 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

Automatic Recognition of Personality Traits

Yuryevich, S. M., Ultes, S., & Schmitt, A. (2014) · doi:10.1145/2668024.2668028 · 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

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

Combining Modality-Specific Extreme Learning Machines for Emotion Recognition in the Wild

Kaya, H., & Salah, A. A. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2666273 · Cited by 17

Social Signal Interpretation (SSI)

Wagner, J., Lingenfelser, F., Bee, N., & André, E. (2011) · Künstliche Intell. · doi:10.1007/s13218-011-0115-x · Cited by 17

Audio-Based Emotion Recognition from Natural Conversations Based on Co-Occurrence Matrix and Frequency Domain Energy Distribution Features

Sayedelahl, A., Fewzee, P., Kamel, M. S., & Karray, F. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_52 · Cited by 17

Emotion Unfolding and Affective Scenes

Danieli, M., Riccardi, G., & Alam, F. (2015) · doi:10.1145/2829966.2829967 · Cited by 17

TCS-ILAB - MediaEval 2015: Affective Impact of Movies and Violent Scene Detection

Chakraborty, R., Maurya, A. K., Pandharipande, M. A., Hassan, E., Ghosh, H., & Kopparapu, S. K. (2015) · MediaEval · Cited by 17

Using representation learning and out-of-domain data for a paralinguistic speech task

Milde, B., & Biemann, C. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-192 · Cited by 17

Gender-Aware CNN-BLSTM for Speech Emotion Recognition

Zhang, L., Wang, L., Dang, J., Guo, L., & Yu, Q. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01418-6_76 · Cited by 17

Speech Emotion Recognition: A Review

Thakur, A., & Dhull, S. K. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-5341-7_61 · Cited by 17

Automatic speaker independent dysarthric speech intelligibility assessment system

Tripathi, A., Bhosale, S., & Kopparapu, S. K. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101213 · Cited by 18

The ACM Multimedia 2023 Computational Paralinguistics Challenge: Emotion Share & Requests

Schuller, B. W., Batliner, A. M., Amiriparian, S., Barnhill, A., Gerczuk, M., Triantafyllopoulos, A., Baird, A., Tzirakis, P., Gagne, C., Cowen, A., et al. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612835 · Cited by 17

Designing an Emotion Detection System for a Socially Intelligent Human-Robot Interaction

Chastagnol, C., Clavel, C., Courgeon, M., & Devillers, L. Y. (2013) · Springer eBooks · doi:10.1007/978-1-4614-8280-2_18 · Cited by 17

Acoustic emotion recognition using dynamic Bayesian networks and multi-space distributions

Barra-Chicote, R., Fernández-Martínez, F., Lutfi, S. L., Lucas-Cuesta, J. M., Macías-Guarasa, J., Montero, J. M., San-Segundo, R., & Pardo, J. M. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-109 · Cited by 17

Intrinsic and Extrinsic Evaluation of an Automatic User Disengagement Detector for an Uncertainty-Adaptive Spoken Dialogue System

Forbes-Riley, K., Litman, D. J., Friedberg, H., & Drummond, J. (2012) · D-Scholarship@Pitt (University of Pittsburgh) · Cited by 16

Stressed speech processing: Human vs automatic in non-professional speakers scenario

Shukla, S., Prasanna, S. R. M., & Dandapat, S. (2011) · National Conference on Communications · doi:10.1109/ncc.2011.5734704 · Cited by 16

Acoustic Characteristics of Emotional Speech Using Spectrogram Image Classification

Stolar, M. N., Lech, M., Bolia, R. S., & Skinner, M. (2018) · International Conference on Signal Processing and Communication Systems · doi:10.1109/icspcs.2018.8631752 · Cited by 16

Improving Convolutional Recurrent Neural Networks for Speech Emotion Recognition

Meyer, P., Xu, Z., & Fingscheidt, T. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383513 · Cited by 16

You're Not You When You're Angry: Robust Emotion Features Emerge by Recognizing Speakers

Aldeneh, Z., & Provost, E. M. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3086050 · Cited by 16

A review of paralinguistic information processing for natural speech communication

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Dimensionality reduction for speech emotion features by multiscale kernels

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Amplitude modulation features for emotion recognition from speech

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Feature optimisation for stress recognition in speech

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SVM based Emotional Speaker Recognition using MFCC-SDC Features

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Speech emotion recognition with skew-robust neural networks

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Speaker-independent speech emotion recognition based on random forest feature selection algorithm

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

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GTM-URL contribution to the INTERSPEECH 2009 emotion challenge

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Towards More Reality in the Recognition of Emotional Speech

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INTERSPEECH 2009 Emotion Recognition Challenge evaluation

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

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