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The performance of speech emotion recognition is affected by the differences in data distributions between train (source domain) and test (target domain) sets used to build and evaluate the models.
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D. Ververidis and C. Kotropoulos, · 2004
Earlier work this paper cites.
“Comparing feature sets for acted and spontaneous speech in view of automatic emotion recognition,”
T. Vogt and E. André, · 2005
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“Fuzzy emotion recognition in natural speech dialogue,”
A. Austermann, N. Esau, L. Kleinjohann, and B. Kleinjohann, · 2005
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M. Shami and W. Verhelst, · 2007
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“Anger detection performances based on prosodic and acoustic cues in several corpora,”
L. Vidrascu and L. Devillers, · 2008
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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
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“Recording audio-visual emotional databases from actors: a closer look,”
C. Busso and S.S. Narayanan, · 2008
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“Cross-corpus acoustic emotion recognition: Variances and strategies,”
B. Schuller, B. Vlasenko, F. Eyben, M. Wöllmer, A. Stuhlsatz, A. Wendemuth, and G. Rigoll, · 2010
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“OpenSMILE: the Munich versatile and fast open-source audio feature extractor,”
F. Eyben, M. Wöllmer, and B. Schuller, · 2010
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“Domain adaptation for large-scale sentiment classification: A deep learning approach,”
X. Glorot, A. Bordes, and Y. Bengio, · 2011
Earlier work this paper cites.
“Unsupervised learning in cross-corpus acoustic emotion recognition,”
Z. Zhang, F. Weninger, M. Wollmer, and B. Schuller, · 2011
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“A personalized emotion recognition system using an unsupervised feature adaptation scheme,”
T. Rahman and C. Busso, · 2012
Earlier work this paper cites.
“Unveiling the acoustic properties that describe the valence dimension,”
C. Busso and T. Rahman, · 2012
Earlier work this paper cites.
“Toward effective automatic recognition systems of emotion in speech,”
C. Busso, M. Bulut, and S.S. Narayanan, · 2013
Earlier work this paper cites.
“On acoustic emotion recognition: compensating for covariate shift,”
A. Hassan, R. Damper, and M. Niranjan, · 2013
Cited alongside, same era.
“Sparse autoencoder-based feature transfer learning for speech emotion recognition,”
J. Deng, Z. Zhang, E. Marchi, and B. Schuller, · 2013
Cited alongside, same era.
“The INTERSPEECH 2013 computational paralinguistics challenge: Social signals, conflict, emotion, autism,”
B. Schuller, S. Steidl, A. Batliner, A. Vinciarelli, K. Scherer, F. Ringeval, M. Chetouani, F. Weninger, F. Eyben, E. Marchi, M. Mortillaro, H. Salamin, A. Polychroniou, F. Valente, and S. Kim, · 2013
Cited alongside, same era.
“Say cheese vs. smile: Reducing speech-related variability for facial emotion recognition,”
Y. Kim and E. Mower Provost, · 2014
Cited alongside, same era.
“Introducing shared-hidden-layer autoencoders for transfer learning and their application in acoustic emotion recognition,”
J. Deng, R Xia, Z. Zhang, Y. Liu, and B. Schuller, · 2014
Cited alongside, same era.
“Cross-corpus speech emotion recognition based on transfer non-negative matrix factorization,”
P. Song, W. Zheng, S. Ou, X. Zhang, Y. Jin, J. Liu, and Y. Yu, · 2016
Later among the works it cites.
“Adversarial multi-task learning of deep neural networks for robust speech recognition,”
Y. Shinohara, · 2016
Later among the works it cites.
“Increasing the reliability of crowdsourcing evaluations using online quality assessment,”
A. Burmania, S. Parthasarathy, and C. Busso, · 2016
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“TensorFlow: A system for large-scale machine learning,”
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D.G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, · 2016
Later among the works it cites.
“Jointly predicting arousal, valence and dominance with multi-task learning,”
S. Parthasarathy and C. Busso, · 2017
Later among the works it cites.
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“Autoencoder-based unsupervised domain adaptation for speech emotion recognition,”
J. Deng, Z. Zhang, F. Eyben, and B. Schuller, · 2014
Cited alongside, same era.
“Generative adversarial nets,”
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, · 2014
Cited alongside, same era.
“Accelerating t-SNE using tree-based algorithms,”
L. Van Der Maaten, · 2014
Cited alongside, same era.
“Supervised domain adaptation for emotion recognition from speech,”
M. Abdelwahab and C. Busso, · 2015
Cited alongside, same era.
“Incorporating Nesterov momentum into Adam,”
T. Dozat, · 2015
Cited alongside, same era.
“Cross-corpus speech emotion recognition based on domain-adaptive least-squares regression,”
Y. Zong, W. Zheng, T. Zhang, and X. Huang, · 2016
Cited alongside, same era.
“Domain-adversarial training of neural networks,”
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, · 2016
Cited alongside, same era.
“Ensemble feature selection for domain adaptation in speech emotion recognition,”
M. Abdelwahab and C. Busso, · 2017
Later among the works it cites.
“Incremental adaptation using active learning for acoustic emotion recognition,”
M. Abdelwahab and C. Busso, · 2017
Later among the works it cites.
“Multi-task deep neural network with shared hidden layers: Breaking down the wall between emotion representations,”
Y. Zhang, Y. Liu, F. Weninger, 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.
“Universum autoencoder-based domain adaptation for speech emotion recognition,”
J. Deng, X. Xu, Z. Zhang, S. Frühholz, and B. Schuller, · 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. Mower Provost, · 2017
Later among the works it cites.
“Keras: Deep learning library for Theano and TensorFlow,” https://keras.io/, April 2017
F. Chollet, · 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, · 2018
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