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Emotion recognition is a classic field of research with a typical setup extracting features and feeding them through a classifier for prediction.
“Iemocap: Interactive emotional dyadic motion capture database,”
C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. N. Chang, S. Lee, and S. S. Narayanan, · 2008
Earlier work this paper cites.
“Combination of generative models and svm based classifier for speech emotion recognition,”
S. Chandrakala and C. C. Sekhar, · 2009
Earlier work this paper cites.
“Opensmile: the munich versatile and fast open-source audio feature extractor,”
F. Eyben, M. Wöllmer, and B. Schuller, · 2010
Earlier work this paper cites.
“Survey on speech emotion recognition: Features, classification schemes, and databases,”
M. El Ayadi, M. S. Kamel, and F. Karray, · 2011
Earlier work this paper cites.
“Gans trained by a two time-scale update rule converge to a local nash equilibrium,”
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, · 2011
Earlier work this paper cites.
“Auto-encoding variational bayes,”
D. P. Kingma and M. Welling, · 2013
Earlier work this paper cites.
“Generative adversarial nets,”
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, · 2014
Earlier work this paper cites.
“Emotion detection in speech using deep networks,”
M. R. Amer, B. Siddiquie, C. Richey, and A. Divakaran, · 2014
Earlier work this paper cites.
“Unsupervised representation learning with deep convolutional generative adversarial networks,”
A. Radford, L. Metz, and S. Chintala, · 2015
Earlier work this paper cites.
“Variational autoencoder based anomaly detection using reconstruction probability,”
J. An and S. Cho, · 2015
Earlier work this paper cites.
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, · 2015
Cited alongside, same era.
“Going deeper with convolutions,”
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, · 2015
Cited alongside, same era.
“Infogan: Interpretable representation learning by information maximizing generative adversarial nets,”
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, · 2016
Cited alongside, same era.
“Style transfer for anime sketches with enhanced residual u-net and auxiliary classifier gan,”
L. Zhang, Y. Ji, X. Lin, and C. Liu, · 2017
Cited alongside, same era.
“Segan: Speech enhancement generative adversarial network,”
S. Pascual, A. Bonafonte, and J. Serrà, · 2017
Cited alongside, same era.
“Speech-based diagnosis of autism spectrum condition by generative adversarial network representations,”
J. Deng, N. Cummins, M. Schmitt, K. Qian, F. Ringeval, and B. Schuller, · 2017
Later among the works it cites.
“Learning representations of emotional speech with deep convolutional generative adversarial networks,”
J. Chang and S. Scherer, · 2017
Later among the works it cites.
“Building naturalistic emotionally balanced speech corpus by retrieving emotional speech from existing podcast recordings,”
R. Lotfian and C. Busso, · 2017
Later among the works it cites.
“Msp-improv: An acted corpus of dyadic interactions to study emotion perception,”
C. Busso, S. Parthasarathy, A. Burmania, M. AbdelWahab, N. Sadoughi, and E. M. Provost, · 2017
Later among the works it cites.
“Unsupervised learning approach to feature analysis for automatic speech emotion recognition,”
S. E. Eskimez, Z. Duan, and W. Heinzelman, · 2018
Later among the works it cites.
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“Wasserstein generative adversarial networks,”
M. Arjovsky, S. Chintala, and L. Bottou, · 2017
Cited alongside, same era.
“A hybrid convolutional variational autoencoder for text generation,”
S. Semeniuta, A. Severyn, and E. Barth, · 2017
Cited alongside, same era.
“Adversarial auto-encoders for speech based emotion recognition,”
S. Sahu, R. Gupta, G. Sivaraman, W. AbdAlmageed, and C. Espy-Wilson, · 2017
Cited alongside, same era.
S. Latif, R. Rana, J. Qadir, and J. Epps, · 2017
Cited alongside, same era.
S. Latif, R. Rana, and J. Qadir, · 2018
Later among the works it cites.
“Towards conditional adversarial training for predicting emotions from speech,”
J. Han, Z. Zhang, Z. Ren, F. Ringeval, and B. Schuller, · 2018
Later among the works it cites.
“On enhancing speech emotion recognition using generative adversarial networks,”
S. Sahu, R. Gupta, and C. Espy-Wilson, · 2018
Later among the works it cites.
“Learning priors for adversarial autoencoders,”
H.-P. Wang, W.-J. Ko, and W.-H. Peng, · 2018
Later among the works it cites.