Knowledge enhancement for speech emotion recognition via multi-level acoustic feature
Author:
Affiliation:
1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, People's Republic of China
Funder
National Natural Science Foundation of China
Publisher
Informa UK Limited
Subject
Artificial Intelligence,Human-Computer Interaction,Software
Link
https://www.tandfonline.com/doi/pdf/10.1080/09540091.2024.2312103
Reference32 articles.
1. Badshah A. M. Ahmad J. Rahim N. & Baik S. W. (2017). Speech emotion recognition from spectrograms with deep convolutional neural network. In International conference on platform technology and service (PlatCon) (pp. 1–5).
2. wav2vec 2.0: A framework for self-supervised learning of speech representations;Baevski A.;Advances in Neural Information Processing Systems,2020
3. Bakhshi A. Wong A. S. W. & Chalup S. K. (2020). End-to-end speech emotion recognition based on time and frequency information using deep neural networks. In Proceedings of the 24th European conference on artificial intelligence (ECAI) (pp. 969–975).
4. IEMOCAP: interactive emotional dyadic motion capture database
5. Cai X. Yuan J. Zheng R. Huang L. & Church K. (2021). Speech emotion recognition with multi-task learning. In Proceedings of the 22nd annual conference of the international speech communication association (Interspeech) (pp. 4508–4512).
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