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The goal of continuous emotion recognition is to assign an emotion value to every frame in a sequence of acoustic features.
C. Busso, S. Lee, and S. S. Narayanan, “Using neutral speech models for emotional speech analysis.” in
2007
Earlier work this paper cites.
M. Lewis, J. M. Haviland-Jones, and L. F. Barrett,
2010
Earlier work this paper cites.
M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in
2010
Earlier work this paper cites.
J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio, “Theano: a CPU and GPU math expression compiler,” in
2010
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz
2011
Earlier work this paper cites.
F. Ringeval, A. Sonderegger, J. Sauer, and D. Lalanne, “Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions,” in
2013
Earlier work this paper cites.
P. Cardinal, N. Dehak, A. L. Koerich, J. Alam, and P. Boucher, “ETS system for AVEC 2015 challenge,” in
2015
Earlier work this paper cites.
L. He, D. Jiang, L. Yang, E. Pei, P. Wu, and H. Sahli, “Multimodal affective dimension prediction using deep bidirectional long short-term memory recurrent neural networks,” in
2015
Earlier work this paper cites.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,”
2015
Cited alongside, same era.
H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in
2015
Cited alongside, same era.
2015
Cited alongside, same era.
F. Chollet, “Keras,” https://github.com/fchollet/keras, 2015
2015
Cited alongside, same era.
M. Valstar, J. Gratch, B. Schuller, F. Ringeval, D. Lalanne, M. Torres Torres, S. Scherer, G. Stratou, R. Cowie, and M. Pantic, “AVEC 2016: Depression, mood, and emotion recognition workshop and challenge,” in
2016
Cited alongside, same era.
G. Trigeorgis, F. Ringeval, R. Brueckner, E. Marchi, M. A. Nicolaou, B. Schuller, and S. Zafeiriou, “Adieu features? end-to-end speech emotion recognition using a deep convolutional recurrent network,” in
2016
Later among the works it cites.
2016
Later among the works it cites.
2016
Later among the works it cites.
V. Dumoulin and F. Visin, “A guide to convolution arithmetic for deep learning,”
2016
Later among the works it cites.
W. Chen, Z. Fu, D. Yang, and J. Deng, “Single-image depth perception in the wild,” in
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K. Brady, Y. Gwon, P. Khorrami, E. Godoy, W. Campbell, C. Dagli, and T. S. Huang, “Multi-modal audio, video and physiological sensor learning for continuous emotion prediction,” in
2016
Cited alongside, same era.
F. Povolny, P. Matejka, M. Hradis, A. Popková, L. Otrusina, P. Smrz, I. Wood, C. Robin, and L. Lamel, “Multimodal emotion recognition for AVEC 2016 challenge,” in
2016
Cited alongside, same era.
F. Eyben, K. R. Scherer, B. W. Schuller, J. Sundberg, E. André, C. Busso, L. Y. Devillers, J. Epps, P. Laukka, S. S. Narayanan
2016
Cited alongside, same era.
D. Le and E. M. Provost, “Emotion recognition from spontaneous speech using hidden markov models with deep belief networks,” in
Cited in the paper.
2016
Later among the works it cites.
S. R. Park and J. Lee, “A fully convolutional neural network for speech enhancement,”
2016
Later among the works it cites.
D. Le, Z. Aldeneh, and E. Mower Provost, “Discretized continuous speech emotion recognition with multi-task deep recurrent neural network,” in
2017
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