Speech emotion classification using feature-level and classifier-level fusion
Author:
Publisher
Springer Science and Business Media LLC
Subject
Control and Optimization,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering
Link
https://link.springer.com/content/pdf/10.1007/s12530-023-09550-9.pdf
Reference44 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 Trans Audio Speech Language Process 22(10):1533–1545
2. Abdelhamid AA, El-Kenawy E-SM, Alotaibi B, Amer GM, Abdelkader MY, Ibrahim A, Eid MM (2022) Robust speech emotion recognition using CNN+ lSTM based on stochastic fractal search optimization algorithm. IEEE Access 10:49265–49284
3. Ancilin J, Milton A (2021) Improved speech emotion recognition with Mel frequency magnitude coefficient. Appl Acoust 179:108046
4. Andayani F, Theng LB, Tsun MT, Chua C (2022) Hybrid lSTM-transformer model for emotion recognition from speech audio files. IEEE Access 10:36018–36027
5. Badshah A M, Ahmad J, Rahim v, Baik S W (2017) Speech emotion recognition from spectrograms with deep convolutional neural network. In: 2017 international conference on platform technology and service (PlatCon), IEEE, pp 1–5
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1. Newman-Watts-Strogatz topology in deep echo state networks for speech emotion recognition;Engineering Applications of Artificial Intelligence;2024-07
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