Speech emotion recognition using the novel SwinEmoNet (Shifted Window Transformer Emotion Network)
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10772-024-10123-7.pdf
Reference53 articles.
1. Abdel-Hamid, L. (2020). Egyptian Arabic speech emotion recognition using prosodic, spectral and wavelet features. Speech Communication, 122, 19–30. https://doi.org/10.1016/j.specom.2020.04.005
2. Aggarwal, A., Srivastava, A., Agarwal, A., Chahal, N., Singh, D., Alnuaim, A. A., Alhadlaq, A., & Lee, H. N. (2022). Two-way feature extraction for speech emotion recognition using deep learning. Sensors, 22(6), 2378. https://doi.org/10.3390/s22062378
3. Akçay, M. B., & Oğuz, K. (2020). Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Communication, 116, 56–76. https://doi.org/10.1016/j.specom.2019.12.001
4. Al-Dujaili, M. J., & Ebrahimi-Moghadam, A. (2023). Speech emotion recognition: A comprehensive survey. Wireless Personal Communications, 129(4), 2525–2561. https://doi.org/10.1007/s11277-023-10244-3
5. Alluhaidan, A. S., Saidani, O., Jahangir, R., Nauman, M. A., & Neffati, O. S. (2023). Speech emotion recognition through hybrid features and convolutional neural network. Applied Sciences, 13(8), 4750. https://doi.org/10.3390/app13084750
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