Coarse-to-Fine Speech Emotion Recognition Based on Multi-Task Learning-Reference-Cited by-同舟云学术

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Coarse-to-Fine Speech Emotion Recognition Based on Multi-Task Learning

Published:2020-06-20 Issue:2-3 Volume:93 Page:299-308
ISSN:1939-8018
Container-title:Journal of Signal Processing Systems
language:en
Short-container-title:J Sign Process Syst

Author:

Huijuan Zhao,Ning Ye,Ruchuan Wang

Funder

National Natural Science Foundation of P. R. China

Graduate Research and Innovation Projects of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Modeling and Simulation,Information Systems,Signal Processing,Theoretical Computer Science,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11265-020-01538-x.pdf

Reference29 articles.

1. Busso, C., Bulut, M., Lee, C., Kazemzadeh, A., Mower, E., Kim, S., Chang, J. N., Lee, S., & Narayanan, S. (2008). IEMOCAP: Interactive emotional dyadic motion capture database. Language Resources and Evaluation, 42(4), 335–359. https://doi.org/10.1007/s10579-008-9076-6.

2. Chen, M., He, X., Yang, J., & Zhang, H. (2018). 3-d convolutional recurrent neural networks with attention model for speech emotion recognition. IEEE Signal Processing Letters, 25(10), 1440–1444. https://doi.org/10.1109/LSP.2018.2860246.

3. Dai, D., Wu, Z., Li, R., Wu, X., Jia, J., & Meng, H. (2019). Learning discriminative features from spectrograms using center loss for speech emotion recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2019, Brighton, United Kingdom, May 12-17, 2019 (pp. 7405–7409), DOI https://doi.org/10.1109/ICASSP.2019.8683765, (to appear in print).

4. Derzhavina, N. M. (2019). Experience of a synthetic approach to an ecological classification of vascular epiphytes. Contemporary Problems of Ecology, 12(5), 434–443.

5. He, X., Song, Y., & Zhang, Y. (2018). A coarse-to-fine scene text detection method based on skeleton-cut detector and binary-tree-search based rectification. Pattern Recognition Letters, 112, 27–33. https://doi.org/10.1016/j.patrec.2018.05.020.

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