Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011
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Human emotions lack temporal boundaries and different individuals express emotions in different ways.
“The task of recognising emotions is challenging because human emotions lack of temporal boundaries and different individuals express emotions in different ways [3].”
From End-to-End Multimodal Emotion Recognition using Deep Neural Networks (2017) · cites this paper as [3]
Cites
The INTERSPEECH 2009 emotion challenge
Schuller, B., Steidl, S., & Batliner, A. (2009) · Interspeech 2009 · doi:10.21437/interspeech.2009-103 · Cited by 938 · see citing works
Neuropsychology of fear and loathing
Calder, A. J., Lawrence, A. D., & Young, A. W. (2001) · Nature reviews. Neuroscience · doi:10.1038/35072584 · Cited by 927 · see citing works
Acoustical properties of speech as indicators of depression and suicidal risk
France, D. J., Shiavi, R. G., Silverman, S., Silverman, M., & Wilkes, M. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.846676 · Cited by 504 · see citing works
Emotion Recognition based on Phoneme Classes
Lee, C. M., Yildirim, S., Bulut, M., Kazemzadeh, A., Busso, C., Deng, Z., Lee, S., & Narayanan, S. (2004) · Interspeech 2004 · doi:10.21437/interspeech.2004-322 · Cited by 228 · see citing works
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Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018 · see citing works
Speech emotion recognition using deep 1D & 2D CNN LSTM networks
Zhao, J., Mao, X., & Chen, L. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.08.035 · Cited by 1,044 · see citing works
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
Tzirakis, P., Trigeorgis, G., Nicolaou, M. A., Schuller, B., & Zafeiriou, S. (2017) · arXiv · doi:10.48550/arxiv.1704.08619 · Cited by 752 · see citing works
Deep learning for robust feature generation in audiovisual emotion recognition
Kim, Y., Lee, H., & Provost, E. M. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638346 · Cited by 401 · see citing works
Mohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391 · see citing works
Emotion-aware connected healthcare big data towards 5G
Hossain, M. S., & Muhammad, G. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2772959 · Cited by 273 · see citing works
Multimodal emotion recognition using deep learning architectures
Ranganathan, H., Chakraborty, S., & Panchanathan, S. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477679 · Cited by 249 · see citing works
Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243 · see citing works
Universum Autoencoder-Based Domain Adaptation for Speech Emotion Recognition
Deng, J., Xu, X., Zhang, Z., Fruhholz, S., & Schuller, B. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2672753 · Cited by 125 · see citing works
End-to-end multimodal affect recognition in real-world environments
Tzirakis, P., Chen, J., Zafeiriou, S., & Schuller, B. (2021) · Information Fusion · doi:10.1016/j.inffus.2020.10.011 · Cited by 105 · see citing works
Audio and face video emotion recognition in the wild using deep neural networks and small datasets
Ding, W., Xu, M., Huang, D., Lin, W., Dong, M., Yu, X., & Li, H. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997637 · Cited by 51 · see citing works
Deep Learning Based Affective Model for Speech Emotion Recognition
Zhou, X., Guo, J., & Bie, R. (2016) · 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld) · doi:10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0133 · Cited by 36 · see citing works
Toward Accountable and Explainable Artificial Intelligence Part Two: The Framework Implementation
Vice, J., & Khan, M. M. (2022) · IEEE Access · doi:10.1109/access.2022.3163523 · Cited by 11 · see citing works
- Authors
- Anagnostopoulos, C.-N., Iliou, T., & Giannoukos, I.
- Affiliations
- University of the Aegean (GR)
- Corresponding author
- Anagnostopoulos, Christos-Nikolaos
- Iliou, Theodoros
- Giannoukos, Ioannis
- Venue
- Artificial Intelligence Review
- Published
- 2012-11-09
- Volume
- 43
- Issue
- 2
- Pages
- 155-177
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2080576537
- Cited by
- 428
- References
- 4
- Citations by year
- 2026: 9 · 2025: 21 · 2024: 25 · 2023: 45 · 2022: 49 · 2021: 48 · 2020: 49 · 2019: 56 · 2018: 55 · 2017: 27 · 2016: 28 · 2015: 9 · 2014: 4 · 2013: 2 · 2012: 1
- FWCI
- 6.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Artificial Intelligence Review (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- emotion recognition from speech, speaker emotion recognition, prosodic features, spectral properties, feature extraction, feature selection, linguistic information, emotion database, acoustic features, paralinguistic information
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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