The DOI matches its Crossref record; 163 of the 163 references it cites were found in a registry or an index.
Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers
Abstract
Abstract Speech is the most natural way of expressing ourselves as humans. It is only natural then to extend this communication medium to computer applications. We define speech emotion recognition (SER) systems as a collection of methodologies that process and classify speech signals to detect the embedded emotions. SER is not a new field, it has been around for over two decades, and has regained attention thanks to the recent advancements. These novel studies make use of the advances in all fields of computing and technology, making it necessary to have an update on the current methodologies and techniques that make SER possible. We have identified and discussed distinct areas of SER, provided a detailed survey of current literature of each, and also listed the current challenges.
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Cites
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Cited by
Do Deepfakes Feel Emotions? A Semantic Approach to Detecting Deepfakes Via Emotional Inconsistencies
Hosler, B., Salvi, D., Murray, A., Antonacci, F., Bestagini, P., Tubaro, S., & Stamm, M. C. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00112 · Cited by 78
Speech Emotion Recognition via an Attentive Time–Frequency Neural Network
Lu, C., Zheng, W., Lian, H., Zong, Y., Tang, C., Li, S., & Zhao, Y. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3219825 · Cited by 51
“RNNs, especially LSTM, have demonstrated their powerful capabilities in modeling the long-range dependence of the time series signal, and have been widely used in speech signal processing [13], [14], [15].”
BackgroundYounis, H. A., Ruhaiyem, N. I. R., Badr, A. A., Abdul-Hassan, A. K., Alfadli, I. M., Binjumah, W. M., Altuwaijri, E. A., & Nasser, M. (2023) · Processes · doi:10.3390/pr11051488 · Cited by 21
Metadata
- Authors
- Akçay, M. B., & Oğuz, K.
- Venue
- Speech Communication
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 116
- Pages
- 56-76
- ISSN
- 0167-6393 (ISSN-L)
- OpenAlex
- W2997399314
- Cited by
- 721
- References
- 163
- FWCI
- 84.48
- Type
- journal article
- Language
- en
- Open access
- closed
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- Akçay, M. B., & Oğuz, K. (2020). Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Communication. https://doi.org/10.1016/j.specom.2019.12.001
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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