CCTG-NET: Contextualized Convolutional Transformer-GRU Network for speech emotion recognition-Reference-Cited by-同舟云学术

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CCTG-NET: Contextualized Convolutional Transformer-GRU Network for speech emotion recognition

Published:2023-12 Issue:4 Volume:26 Page:1099-1116
ISSN:1381-2416
Container-title:International Journal of Speech Technology
language:en
Short-container-title:Int J Speech Technol

Author:

Tellai Mohammed,Mao Qirong

Funder

National Natural Science Foundation of China

Jiangsu Provincial Key Research and Development Program

Publisher

Springer Science and Business Media LLC

Subject

Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software

Link

https://link.springer.com/content/pdf/10.1007/s10772-023-10080-7.pdf

Reference65 articles.

1. Afrillia, Y., Mawengkang, H., Ramli, M., & Fhonna, R. P. (2017). Performance measurement of Mel frequency ceptral coefficient (MFCC) method in learning system of Al-Qur’an based in nagham pattern recognition. Journal of Physics: Conference Series, 930, 012036.

2. Aftab, A., Morsali, A., Ghaemmaghami, S., & Champagne, B. (2022). Light-sernet: A lightweight fully convolutional neural network for speech emotion recognition. In ICASSP 2022-2022 IEEE International conference on acoustics, speech and signal processing (ICASSP) (pp. 6912–6916). IEEE.

3. Anagnostopoulos, C.-N., Iliou, T., & Giannoukos, I. (2015). Features and classifiers for emotion recognition from speech: A survey from 2000 to 2011. Artificial Intelligence Review, 43(2), 155–177.

4. Araujo, A., Norris, W., & Sim, J. (2019). Computing receptive fields of convolutional neural networks. Distill, 4(11), 21.

5. Barsade, S. G. (2002). The ripple effect: Emotional contagion and its influence on group behavior. Administrative Science Quarterly, 47(4), 644–675.

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