Cross-Corpus Speech Emotion Recognition Based on Causal Emotion Information Representation
Author:
Affiliation:
1. Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology
2. School of Communication Engineering, Nanjing Institute of Technology
3. IFLYTEK Research
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/transinf/E107.D/8/E107.D_2023EDL8087/_pdf
Reference17 articles.
1. [1] F.A.D. Rí, F.C. Ciardi, and N. Conci, “Speech emotion recognition and deep learning: an extensive validation using convolutional neural networks,” IEEE Access, vol.11, pp.116638-116649, 2023. 10.1109/access.2023.3326071
2. [2] W. Zhang and P. Song, “Transfer sparse discriminant subspace learning for cross-corpus speech emotion recognition,” IEEE/ACM Trans. Audio Speech Lang. Process., vol.28, pp.307-318, 2020. 10.1109/taslp.2019.2955252
3. [3] S. Li, P. Song, L. Ji, Y. Jin, and W. Zheng, “A generalized subspace distribution adaptation framework for cross-corpus speech emotion recognition,” ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, pp.1-5, 2023. 10.1109/icassp49357.2023.10097258
4. [4] Y. Gao, S. Okada, L. Wang, J. Liu, and J. Dang, “Domain-invariant feature learning for cross corpus speech emotion recognition,” ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, pp.6427-6431, 2022. 10.1109/icassp43922.2022.9747129
5. [5] J. Zhang, L. Jiang, Y. Zong, W. Zheng, and L. Zhao, “Cross-corpus speech emotion recognition using joint distribution adaptive regression,” ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Canada, pp.3790-3794, 2021. 10.1109/icassp39728.2021.9414372
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