Speech emotion recognition with unsupervised feature learning

Author:

Huang Zheng-wei,Xue Wen-tao,Mao Qi-rong

Funder

National Natural Science Foundation of China

Six Talent Peaks Foundation of Jiangsu Province, China

Publisher

Zhejiang University Press

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing

Reference33 articles.

1. Abdel-Hamid, O., Mohamed, A.R., Jiang, H., et al., 2012. Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.4277–4280. [doi: 10.1109/ICASSP.2012.6288864 ]

2. Burkhardt, F., Paeschke, A., Rolfes, M., et al., 2005. A database of German emotional speech. Interspeech, p.1517–1520.

3. Chan, T.H., Jia, K., Gao, S., et al., 2014. PCANet: a simple deep learning baseline for image classification? arXiv preprint, arXiv:1404.3606.

4. Coates, A., Ng, A.Y., Lee, H., 2011. An analysis of singlelayer networks in unsupervised feature learning. Int. Conf. on Artificial Intelligence and Statistics, p.215–223.

5. Dahl, G.E., Yu, D., Deng, L., et al., 2012. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Lang. Process., 20(1):30–42. [doi: 10.1109/TASL.2011.2134090 ]

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