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Representation learning for speech emotion recognition is challenging due to labeled data sparsity issue and lack of gold standard references.
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“Iemocap: Interactive emotional dyadic motion capture database,”
Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N Chang, Sungbok Lee, and Shrikanth S Narayanan, · 2008
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Carlos Busso, Angeliki Metallinou, and Shrikanth S Narayanan, · 2011
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“Generative adversarial nets,”
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, · 2014
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“A pitch extraction algorithm tuned for automatic speech recognition,”
Pegah Ghahremani, Bagher BabaAli, Daniel Povey, Korbinian Riedhammer, Jan Trmal, and Sanjeev Khudanpur, · 2014
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“Leveraging valence and activation information via multi-task learning for categorical emotion recognition,”
Rui Xia and Yang Liu, · 2015
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“High-level feature representation using recurrent neural network for speech emotion recognition,”
Jinkyu Lee and Ivan Tashev, · 2015
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“Domain-adversarial training of neural networks,”
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky, · 2016
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“Representation learning for speech emotion recognition.,”
Sayan Ghosh, Eugene Laksana, Louis-Philippe Morency, and Stefan Scherer, · 2016
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“Adieu features? end-to-end speech emotion recognition using a deep convolutional recurrent network,”
George Trigeorgis, Fabien Ringeval, Raymond Brueckner, Erik Marchi, Mihalis A Nicolaou, Björn Schuller, and Stefanos Zafeiriou, · 2016
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“Adversarial multi-task learning of deep neural networks for robust speech recognition.,”
Yusuke Shinohara, · 2016
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“Unsupervised latent behavior manifold learning from acoustic features: Audio2behavior,”
Haoqi Li, Brian Baucom, and Panayiotis Georgiou, · 2017
Cited alongside, same era.
“Cross-corpus acoustic emotion recognition with multi-task learning: Seeking common ground while preserving differences,”
Biqiao Zhang, Emily Mower Provost, and Georg Essl, · 2017
Cited alongside, same era.
“Learning representations of emotional speech with deep convolutional generative adversarial networks,”
Jonathan Chang and Stefan Scherer, · 2017
Cited alongside, same era.
“An unsupervised deep domain adaptation approach for robust speech recognition,”
Sining Sun, Binbin Zhang, Lei Xie, and Yanning Zhang, · 2017
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“Domain adversarial for acoustic emotion recognition,”
Mohammed Abdelwahab and Carlos Busso, · 2018
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“Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph,”
AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency, · 2018
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“An end-to-end deep learning framework for speech emotion recognition of atypical individuals.,”
Dengke Tang, Junlin Zeng, and Ming Li, · 2018
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“Speech emotion recognition from variable-length inputs with triplet loss function.,”
Jian Huang, Ya Li, Jianhua Tao, Zhen Lian, et al., · 2018
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“An unsupervised autoregressive model for speech representation learning,”
Yu-An Chung, Wei-Ning Hsu, Hao Tang, and James Glass, · 2019
Closest in time.
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Che-Wei Huang, Shrikanth Narayanan, et al., · 2017
Cited alongside, same era.
“Progressive neural networks for transfer learning in emotion recognition,”
John Gideon, Soheil Khorram, Zakaria Aldeneh, Dimitrios Dimitriadis, and Emily Mower Provost, · 2017
Cited alongside, same era.
“Speech emotion recognition: Two decades in a nutshell, benchmarks, and ongoing trends,”
Björn W Schuller, · 2018
Cited alongside, same era.
“On enhancing speech emotion recognition using generative adversarial networks,”
Saurabh Sahu, Rahul Gupta, and Carol Espy-Wilson, · 2018
Cited alongside, same era.
“Unsupervised domain adaptation via domain adversarial training for speaker recognition,”
Qing Wang, Wei Rao, Sining Sun, Leib Xie, Eng Siong Chng, and Haizhou Li, · 2018
Cited alongside, same era.
“Speaker-invariant training via adversarial learning,”
Zhong Meng, Jinyu Li, Zhuo Chen, Yang Zhao, Vadim Mazalov, Yifan Gang, and Biing-Hwang Juang, · 2018
Cited alongside, same era.
Santiago Pascual, Mirco Ravanelli, Joan Serrà, Antonio Bonafonte, and Yoshua Bengio, · 2019
Closest in time.
“Towards adversarial learning of speaker-invariant representation for speech emotion recognition,”
Ming Tu, Yun Tang, Jing Huang, Xiaodong He, and Bowen Zhou, · 2019
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“Transferable adversarial training: A general approach to adapting deep classifiers,”
Hong Liu, Mingsheng Long, Jianmin Wang, and Michael Jordan, · 2019
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“Improving speech emotion recognition with unsupervised representation learning on unlabeled speech,”
Michael Neumann and Ngoc Thang Vu, · 2019
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“Learning discriminative features from spectrograms using center loss for speech emotion recognition,”
Dongyang Dai, Zhiyong Wu, Runnan Li, Xixin Wu, Jia Jia, and Helen Meng, · 2019
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“Multi-modal learning for speech emotion recognition: An analysis and comparison of asr outputs with ground truth transcription,”
Saurabh Sahu, Vikramjit Mitra, Nadee Seneviratne, and Carol Espy-Wilson, · 2019
Closest in time.