Speech emotion recognition with transfer learning and multi-condition training for noisy environments
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10772-024-10109-5.pdf
Reference23 articles.
1. Al-Dujaili, M. J., & Ebrahimi-Moghadam, A. (2023). Speech emotion recognition: A comprehensive survey. Wireless Personal Communications, 129(4), 2525–2561.
2. Amiriparian, S., Gerczuk, M., Ottl, S., Cummins, N., Pugachevskiy, S., & Schuller, B. (2018). Bag-of-deep-features: Noise-robust deep feature representations for audio analysis. In 2018 international joint conference on neural networks (IJCNN2018) (pp. 1–7). IEEE.
3. Azarang, A., & Kehtarnavaz, N. (2020). A review of multi-objective deep learning speech denoising methods. Speech Communication, 122, 1–10.
4. Badshah, A. M., Ahmad, J., Rahim, N., & Baik, S. W. (2017). Speech emotion recognition from spectrograms with deep convolutional neural network. In 2017 international conference on platform technology and service (PlatCon 2017) (pp. 1–5). IEEE.
5. Bentler, R., & Chiou, L.-K. (2006). Digital noise reduction: An overview. Trends in Amplification, 10(2), 67–82.
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