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Recently, generative adversarial networks and adversarial autoencoders have gained a lot of attention in machine learning community due to their exceptional performance in tasks such as digit classification and face recognition.
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“Analysis of emotion recognition using facial expressions, speech and multimodal information,”
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“Speech emotion recognition based on hmm and svm,”
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“Emotional speech recognition: Resources, features, and methods,”
D. Ververidis and C. Kotropoulos, · 2006
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“Emotion recognition from noisy speech,”
M. You, C. Chen, J. Bu, J. Liu, and J. Tao, · 2006
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“Gmm supervector based svm with spectral features for speech emotion recognition,”
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“Activity and emotion recognition to support early diagnosis of psychiatric diseases,”
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“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
Cited alongside, same era.
“The interspeech 2010 paralinguistic challenge.,”
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“Emotion in medicine,”
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“Survey on speech emotion recognition: Features, classification schemes, and databases,”
M. El Ayadi, M. S. Kamel, and F. Karray, · 2011
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“Emotion recognition using a hierarchical binary decision tree approach,”
C.-C. Lee, E. Mower, C. Busso, S. Lee, and S. Narayanan, · 2011
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“Deep neural networks for acoustic emotion recognition: raising the benchmarks,”
A. Stuhlsatz, C. Meyer, F. Eyben, T. Zielke, G. Meier, and B. Schuller, · 2011
Using denoising autoencoder for emotion recognition
R. Xia and Y. Liu, · 2013
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“Recent developments in opensmile, the munich open-source multimedia feature extractor,”
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“Speech emotion recognition using a deep autoencoder,”
N. E. Cibau, E. M. Albornoz, and H. L. Rufiner, · 2013
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“Generative adversarial nets,”
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, · 2014
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“Robust unsupervised arousal rating: A rule-based framework withknowledge-inspired vocal features,”
D. Bone, C.-C. Lee, and S. Narayanan, · 2014
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Cited alongside, same era.
“The interspeech 2011 speaker state challenge.,”
B. W. Schuller, S. Steidl, A. Batliner, F. Schiel, and J. Krajewski, · 2011
Cited alongside, same era.
“Deap: A database for emotion analysis; using physiological signals,”
S. Koelstra, C. Muhl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, and I. Patras, · 2012
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“Autoencoders, unsupervised learning, and deep architectures.,”
P. Baldi, · 2012
Cited alongside, same era.
“Ann based emotion recognition,”
M. Singh, M. M. Singh, and N. Singhal, · 2013
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A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, · 2015
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“Learning representations of affect from speech,”
S. Ghosh, E. Laksana, L.-P. Morency, and S. Scherer, · 2015
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“Attention assisted discovery of sub-utterance structure in speech emotion recognition,”
C. W. Huang and S. S. Narayanan, · 2016
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“Analysis of engagement behavior in children during dyadic interactions using prosodic cues,”
R. Gupta, D. Bone, S. Lee, and S. Narayanan, · 2016
Later among the works it cites.