Optimized cross-corpus speech emotion recognition framework based on normalized 1D convolutional neural network with data augmentation and feature selection
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software
Link
https://link.springer.com/content/pdf/10.1007/s10772-023-10063-8.pdf
Reference40 articles.
1. Abdel-Hamid, O., Mohamed, A. R., Jiang, H., Deng, L., Penn, G., & Yu, D. (2014). Convolutional neural networks for speech recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(10), 1533–1545.
2. Ancilin, J., & Milton, A. (2021). Improved speech emotion recognition with Mel frequency magnitude coefficient. Applied Acoustics, 179, 108046.
3. Anvarjon, T., & Kwon, S. (2020). Deep-net: A lightweight CNN-based speech emotion recognition system using deep frequency features. Sensors, 20(18), 5212.
4. Bertero, D., & Fung, P. (2017). A first look into a convolutional neural network for speech emotion detection. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5115–5119). IEEE.
5. Chakraborty, R., Pandharipande, M., & Kopparapu, S. K. (2016). Knowledge-based framework for intelligent emotion recognition in spontaneous speech. Procedia Computer Science, 96, 587–596.
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