Feature fusion methods research based on deep belief networks for speech emotion recognition under noise condition-Reference-Cited by-同舟云学术

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Feature fusion methods research based on deep belief networks for speech emotion recognition under noise condition

Published:2017-12-02 Issue:5 Volume:10 Page:1787-1798
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Huang Yongming,Tian Kexin,Wu Ao,Zhang Guobao

Funder

open Fund of Jiangsu Province Natural Science Foundation

National Natural Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s12652-017-0644-8.pdf

Reference45 articles.

1. Ali Hassan R, Damper, Niranjan M (2013) On acoustic emotion recognition: compensating for covariate shift. IEEE Trans Audio Speech Lang Process 21(7):1458–1468

2. Atal BS (1974) Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification. J Acoust Soc Am 55(6):1304–1312

3. Bahreini K, Nadolski R, Westera W (2016) Towards multimodal emotion recognition in e-learning environments. Inter Learning Environ 24(3):590–605

4. Bengio Y (2009) Learning deep architectures for AI. Now Publ Inc 2(1):67–76

5. Bengio Y (2012) Deep learning of representations for unsupervised and transfer learning. J Mach Learning Res Proc Track 27(2), 17–36

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