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Replacing hand-engineered pipelines with end-to-end deep learning systems has enabled strong results in applications like speech and object recognition.
Optimal estimators for spectral restoration of noisy speech
J. Porter and S. Boll · 1984
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Bidirectional recurrent neural networks
Mike Schuster and Kuldip K Paliwal · 1997
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Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber · 2006
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Neural machine translation by jointly learning to align and translate
K. Cho D. Bahdanau and Y. Bengio · 2014
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Deep speech 2: End-to-end speech recognition in english and mandarin
Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, et al · 2015
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A time delay neural network architecture for efficient modeling of long temporal contexts
Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur · 2015
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Fast and accurate recurrent neural network acoustic models for speech recognition
Hasim Sak, Andrew Senior, Kanishka Rao, and Francoise Beaufays · 2015
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Learning the speech front-end with raw waveform cldnns
Tara N. Sainath, Ron J. Weiss, Andrew W. Senior, Kevin W. Wilson, and Oriol Vinyals · 2015
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Achieving human parity in conversational speech recognition
Wayne Xiong, Jasha Droppo, Xuedong Huang, Frank Seide, Mike Seltzer, Andreas Stolcke, Dong Yu, and Geoffrey Zweig · 2016
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Dense prediction on sequences with time-dilated convolutions for speech recognition
Tom Sercu and Vaibhava Goel · 2016
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Highway long short-term memory rnns for distant speech recognition
Yu Zhang, Guoguo Chen, Dong Yu, Kaisheng Yao, Sanjeev Khudanpur, and James R. Glass · 2016
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Learning multiscale features directly from waveforms
Zhenyao Zhu, Jesse Engel, and Awni Y. Hannun · 2016
Segmental recurrent neural networks for end-to-end speech recognition
Liang Lu, Lingpeng Kong, Chris Dyer, Noah A. Smith, and Steve Renals · 2016
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Purely sequence-trained neural networks for asr based on lattice-free mmi
Daniel Povey, Vijayaditya Peddinti, Daniel Galvez, Pegah Ghahremani, Vimal Manohar, Xingyu Na, Yiming Wang, and Sanjeev Khudanpur · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Layer Normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Tim Cooijmans, Nicolas Ballas, César Laurent, and Aaron C. Courville · 2016
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Training deep bidirectional lstm acoustic model for lvcsr by a context-sensitive-chunk bptt approach
Kai Chen and Qiang Huo · 2016
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Listen, attend and spell: A neural network for large vocabulary conversational speech recognition
William Chan, Navdeep Jaitly, Quoc Le, and Oriol Vinyals · 2016
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Wav2letter: an end-to-end convnet-based speech recognition system
Ronan Collobert, Christian Puhrsch, and Gabriel Synnaeve · 2016
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Trainable frontend for robust and far-field keyword spotting
Yuxuan Wang, Pascal Getreuer, Thad Hughes, Richard F Lyon, and Rif A Saurous
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Lookahead convlution layer for unidirectional recurrent neural networks
Chong Wang, Dani Yogatama, Adam Coates, Tony Han, Awni Hannun, and Bo Xiao
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Normalization propagation: A parametric technique for removing internal covariate shift in deep networks
Devansh Arpit, Yingbo Zhou, Bhargava U. Kota, and Venu Govindaraju · 2016
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Gram-ctc: Automatic unit selection and target decomposition for sequence labelling
Hairong Liu, Zhenyao Zhu, Xiangang Li, and Sanjeev Satheesh · 2017
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A study on data augmentation of reverberant speech for robust speech recognition
Tom Ko, Vijayaditya Peddinti, Daniel Povey, Michael Seltzer, and Sanjeev Khudanpur · 2017
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