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Deep learning approaches have been widely used in Automatic Speech Recognition (ASR) and they have achieved a significant accuracy improvement.
A tutorial on hidden markov models and selected applications in speech recognition
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LeCun, Yann and Bengio, Yoshua · 1998
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Graves, Alex and Schmidhuber, Jürgen · 2005
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Abdel-Hamid, Ossama, Mohamed, Abdel-rahman, Jiang, Hui, Deng, Li, Penn, Gerald, and Yu, Dong · 2014
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Deep speech: Scaling up end-to-end speech recognition
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Sak, Hasim, Senior, Andrew W, and Beaufays, Françoise · 2014
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Simonyan, Karen and Zisserman, Andrew · 2014
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Yu, Dong and Deng, Li · 2014
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Deep speech 2: End-to-end speech recognition in english and mandarin
Amodei, Dario, Anubhai, Rishita, Battenberg, Eric, Case, Carl, Casper, Jared, Catanzaro, Bryan, Chen, Jingdong, Chrzanowski, Mike, Coates, Adam, Diamos, Greg, et al · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
Cited alongside, same era.
Eesen: End-to-end speech recognition using deep rnn models and wfst-based decoding
Miao, Yajie, Gowayyed, Mohammad, and Metze, Florian · 2015
Cited alongside, same era.
Very deep convolutional networks for end-to-end speech recognition
Zhang, Yu, Chan, William, and Jaitly, Navdeep
Cited in the paper.
Deep recurrent convolutional neural network: Improving performance for speech recognition
Zhang, Zewang, Sun, Zheng, Liu, Jiaqi, Chen, Jingwen, Huo, Zhao, and Zhang, Xiao
Cited in the paper.
Deep convolutional neural networks with layer-wise context expansion and attention
Yu, Dong, Xiong, Wayne, Droppo, Jasha, Stolcke, Andreas, Ye, Guoli, Li, Jinyu, and Zweig, Geoffrey · 2016
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Zagoruyko, Sergey and Komodakis, Nikos · 2016
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Residual lstm: Design of a deep recurrent architecture for distant speech recognition
Kim, Jaeyoung, El-Khamy, Mostafa, and Lee, Jungwon · 2017
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