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Generative Adversarial Networks (GANs) have gained a lot of attention from machine learning community due to their ability to learn and mimic an input data distribution.
“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.
“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.
“Classification of emotional content of sighs in dyadic human interactions,”
R. Gupta, C.-C. Lee, and S. Narayanan, · 2012
Earlier work this paper cites.
“Recent developments in opensmile, the munich open-source multimedia feature extractor,”
F. Eyben, F. Weninger, F. Gross, and B. Schuller, · 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.
“Conditional generative adversarial nets,”
M. Mirza and S. Osindero, · 2014
Earlier work this paper cites.
“Predicting client’s inclination towards target behavior change in motivational interviewing and investigating the role of laughter,”
R. Gupta, P. G. Georgiou, D. C. Atkins, and S. S. Narayanan, · 2014
Cited alongside, same era.
“Unsupervised representation learning with deep convolutional generative adversarial networks,”
A. Radford, L. Metz, and S. Chintala, · 2015
Cited alongside, same era.
“Emotion recognition during speech using dynamics of multiple regions of the face,”
Y. Kim and E. M. Provost, · 2015
Cited alongside, same era.
“Generative image modeling using style and structure adversarial networks,”
X. Wang and A. Gupta, · 2016
Cited alongside, same era.
“Nips 2016 tutorial: Generative adversarial networks,”
I. Goodfellow, · 2016
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
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.
“Image-to-image translation with conditional adversarial networks,”
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, · 2017
Later among the works it cites.
“Adversarial learning for neural dialogue generation,”
J. Li, W. Monroe, T. Shi, S. Jean, A. Ritter, and D. Jurafsky, · 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.
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“Predicting affective dimensions based on self assessed depression severity.,”
R. Gupta and S. S. Narayanan, · 2016
Cited alongside, same era.
“open-source media interpretation by large feature-space extraction,”
F. Eyben, F. Weninger, M. Wöllmer, and B. Schuller,
Cited in the paper.