Emotion recognition of audio/speech data using deep learning approaches-Reference-Cited by-同舟云学术

Emotion recognition of audio/speech data using deep learning approaches

Published:2020-08-17 Issue:6 Volume:41 Page:1309-1317
ISSN:0252-2667
Container-title:Journal of Information and Optimization Sciences
language:en
Short-container-title:Journal of Information and Optimization Sciences

Author:

Gupta Vedika¹,Juyal Stuti¹,Singh Gurvinder Pal¹,Killa Chirag¹,Gupta Nishant¹

Affiliation:

1. Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi 110063, India

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/02522667.2020.1809089

Reference18 articles.

1. Vogt, T., André, E. & Wagner, J. (2008). Automatic recognition of emotions from speech: a review of the literature and recommendations for practical realization. In Affect and emotion in human-computer interaction (pp. 75-91). Springer, Berlin, Heidelberg.

2. Wang, K., An, N., Li, B. N., Zhang, Y. & Li, L. (2015). Speech emotion recognition using Fourier parameters. IEEE Transactions on Affective Computing, 6(1), 69-75. Z. Xiao, E. Dellandrea, W. Dou, and L. Chen, “Features extraction and selection for emotional speech classification,” in Advanced Video and Signal Based Surveillance, 2005. AVSS 2005. IEEE Conference on, 2005, pp. 411-416.

3. Olatunji, S. O. (2019). Improved email spam detection model based on support vector machines. Neural Computing and Applications, 31(3), 691-699.

4. [4] Koolagudi, S. G. & Rao, K. S. (2010, December). Real life emotion classification using voice and pitch based spectral features. In 2010 Annual IEEE India Conference (INDICON) (pp. 1-4). IEEE.

5. Lim, W., Jang, D. & Lee, T. (2016, December). Speech emotion recognition using convolutional and recurrent neural networks. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) (pp. 1-4). IEEE.

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