A. Milton, S. S. Roy, and S. T. Selvi, Svm scheme for speech emotion recognition using mfcc feature, International Journal of Computer Applications, vol.69, issue.9

W. Zhang, D. Zhao, Z. Chai, L. T. Yang, X. Liu et al., Deep learning and svm-based emotion recognition from chinese speech for smart affective services, Software: Practice and Experience, vol.47, issue.8, pp.1127-1138, 2017.

R. Chen, Y. Zhou, and Y. Qian, Emotion recognition using support vector machine and deep neural network, National Conference on Man-Machine Speech Communication, pp.122-131, 2017.

G. D. Sree, P. Chandrasekhar, and B. Venkateshulu, Svm based speech emotion recognition compared with gmm-ubm and nn, International Journal of Engineering Science, vol.3293

S. Prasomphan, Improvement of speech emotion recognition with neural network classifier by using speech spectrogram, Systems, Signals and Image Processing, pp.73-76, 2015.

W. Lim, D. Jang, and T. Lee, Speech emotion recognition using convolutional and recurrent neural networks, in: Signal and Information Processing Association Annual Summit and Conference, p.2016

A. , , pp.1-4, 2016.

S. Mirsamadi, E. Barsoum, and C. Zhang, Automatic speech emotion recognition using recurrent neural networks with local attention, Acoustics, Speech and Signal Processing, pp.2227-2231, 2017.

L. Kerkeni, Y. Serrestou, M. Mbarki, K. Raoof, and M. A. Mahjoub, A review on speech emotion recognition: Case of pedagogical interaction in classroom, Advanced Technologies for Signal and Image Processing, pp.1-7, 2017.

L. Kerkeni, Y. Serrestou, M. Mbarki, M. Mahjoub, K. Raoof et al., Speech emotion recognition: Recurrent neural networks compared to svm and linear regression, Artificial Neural Networks and Machine Learning, 2017. ICANN 2017. International Conference on, pp.451-453, 2017.

C. Wang and Y. Kang, Feature extraction techniques of non-stationary signals for fault diagnosis in machinery systems, Journal of Signal and Information Processing, vol.3, issue.01, p.16, 2012.

A. F. Haque, Frequency analysis and feature extraction of impressive tools, International Journal of Advance Innovations, vol.2, issue.2, p.1, 2013.

M. Nayak and B. S. Panigrahi, Advanced signal processing techniques for feature extraction in data mining, International Journal of Computer Applications, vol.19, issue.9, pp.30-37, 2011.

R. Fonseca-pinto, A new tool for nonstationary and nonlinear signals: The hilbert-huang transform in biomedical applications, Communications and Software, 2011.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih et al., The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol.454, pp.903-995, 1998.

X. Li and X. Li, Speech emotion recognition using novel hht-teo based features, JCP, vol.6, issue.5, pp.989-998, 2011.

X. Li, X. Li, X. Zheng, and D. Zhang, Emd-teo based speech emotion recognition, Life System Modeling and Intelligent Computing, pp.180-189, 2010.

C. Shahnaz, S. Sultana, S. A. Fattah, R. M. Rafi, I. Ahmmed et al., Emotion recognition based on emd-wavelet analysis of speech signals, 2015 IEEE International Conference on, pp.307-310, 2015.

N. Zhuang, Y. Zeng, L. Tong, C. Zhang, H. Zhang et al., Emotion recognition from eeg signals using multidimensional information in emd domain, 2017.

R. Sharma, L. Vignolo, G. Schlotthauer, M. A. Colominas, H. L. Rufiner et al., Empirical mode decomposition for adaptive am-fm analysis of speech: A review, Speech Communication, 2017.

Z. Wu and N. E. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis method, Advances in adaptive data analysis, vol.1, issue.01, pp.1-41, 2009.

S. G. Goel, Speech emotion recognition using eemd, svm & ann, 2014.

I. Antoniadou, G. Manson, N. Dervilis, T. Barszcz, W. Staszewski et al., Use of the teager-kaiser energy operator for condition monitoring of a wind turbine gearbox, ISMA 2012, including USD 2012: International Conference on Uncertainty in Structure Dynamics, vol.6, pp.4255-4268, 2012.

K. Khaldi, A. Boudraa, and A. Komaty, Speech enhancement using empirical mode decomposition and the teager-kaiser energy operator, The Journal of the Acoustical Society of America, vol.135, issue.1, pp.451-459, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01084175

J. Tang, S. Alelyani, and H. Liu, Feature selection for classification: A review, Data Classification: Algorithms and Applications, p.37, 2014.

L. Kerkeni, Y. Serrestou, M. Mbarki, M. Mahjoub, and K. Raoof, Speech emotion recognition: Methods and cases study, International Conference on Agents and Artificial Intelligence (ICAART), 2018.

P. Maragos, J. F. Kaiser, and T. F. Quatieri, Energy separation in signal modulations with application to speech analysis, IEEE transactions on signal processing, vol.41, issue.10, pp.3024-3051, 1993.

A. Potamianos and P. Maragos, Speech analysis and synthesis using an am-fm modulation model1, Speech communication, vol.28, issue.3, pp.195-209, 1999.

V. Sethu, E. Ambikairajah, and J. Epps, Empirical mode decomposition based weighted frequency feature for speech-based emotion classification, Acoustics, Speech and Signal Processing, pp.5017-5020, 2008.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih et al., The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London A: mathematical, physical and engineering sciences, vol.454, pp.903-995, 1998.

P. Maragos, J. F. Kaiser, and T. F. Quatieri, On amplitude and frequency demodulation using energy operators, IEEE Transactions on signal processing, vol.41, issue.4, pp.1532-1550, 1993.

P. Maragos, T. F. Quatieri, and J. F. Kaiser, Speech nonlinearities, modulations, and energy operators, pp.421-424, 1991.

A. Potamianos and P. Maragos, A comparison of the energy operator and the hilbert transform approach to signal and speech demodulation, Signal processing, vol.37, issue.1, pp.95-120, 1994.

S. Wu, T. H. Falk, and W. Chan, Automatic speech emotion recognition using modulation spectral features, Speech communication, vol.53, issue.5, pp.768-785, 2011.

L. Atlas and S. A. Shamma, Joint acoustic and modulation frequency, EURASIP Journal on Applied Signal Processing, pp.668-675, 2003.

D. W. Aha and R. L. Bankert, Feature selection for case-based classification of cloud types: An empirical comparison, Proceedings of the AAAI-94 workshop on Case-Based Reasoning, vol.106, p.112, 1994.

K. Duan, J. C. Rajapakse, H. Wang, and F. Azuaje, Multiple svm-rfe for gene selection in cancer classification with expression data, IEEE transactions on nanobioscience, vol.4, issue.3, pp.228-234, 2005.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine learning, vol.46, issue.1-3, pp.389-422, 2002.

Y. Pan, P. Shen, and L. Shen, Speech emotion recognition using support vector machine, International Journal of Smart Home, vol.6, issue.2, pp.101-108, 2012.

S. Wu, Recognition of human emotion in speech using modulation spectral features and support vector machines, 2009.

S. R. Gunn, Support vector machines for classification and regression, ISIS technical report, vol.14, issue.1, pp.5-16, 1998.

, Svm and kernel methods matlab toolbox

L. Zhang, S. Wang, and B. Liu, Deep learning for sentiment analysis: A survey, 2018.

S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural computation, vol.9, issue.8, pp.1735-1780, 1997.

S. Chen and Q. Jin, Multi-modal Dimensional Emotion Recognition using Recurrent Neural Networks, 2015.

I. Saratxaga, E. Navas, I. Hernáez, and I. Luengo, Designing and recording an emotional speech database for corpus based synthesis in basque, Proc. of fifth international conference on Language Resources and Evaluation (LREC), pp.2126-2129, 2006.

F. Burkhardt, A. Paeschke, M. Rolfes, W. Sendlmeier, and B. Weiss, A Database of German Emotional Speech, 2005.

, Emotional speech synthesis database

Z. Liu, M. Wu, W. Cao, J. Mao, J. Xu et al., Speech emotion recognition based on feature selection and extreme learning machine decision tree, Neurocomputing, vol.273, pp.271-280, 2018.