Filter-based multi-task cross-corpus feature learning for speech emotion recognition
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02977-2.pdf
Reference13 articles.
1. Zhang, B., Provost, E.M., Essl, G.: Cross-corpus acoustic emotion recognition with multi-task learning: seeking common ground while preserving differences. IEEE Trans. Affect. Comput. 10(1), 85–99 (2019). https://doi.org/10.1109/TAFFC.2017.2684799
2. Swain, M., Routray, A., Kabisatpathy, P.: Databases, features and classifiers for speech emotion recognition: a review. Int. J. Speech Technol. 21(1), 93–120 (2018). https://doi.org/10.1007/s10772-018-9491-z
3. Zhao, Z., Bao, Z., Zhao, Y., Zhang, Z., Cummins, N., Ren, Z., Schuller, B.: Exploring deep spectrum representations via attention-based recurrent and convolutional neural networks for speech emotion recognition. IEEE Access 7, 97515–97525 (2019). https://doi.org/10.1109/ACCESS.2019.2928625
4. Kalhor, E., Bakhtiari, B.: Speaker independent feature selection for speech emotion recognition: a multi-task approach. Multimed. Tools Appl. 80(6), 8127–8146 (2021). https://doi.org/10.1007/s11042-020-10119-w
5. Khalil, R.A., Jones, E., Babar, M.I., Jan, T., Zafar, M.H., Alhussain, T.: Speech emotion recognition using deep learning techniques: a review. IEEE Access 7, 117,327–117,345 (2019). https://doi.org/10.1109/ACCESS.2019.2936124
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