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booktitle={2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
title={Batch normalized recurrent neural networks}, 
year={2016}, 
volume={}, 
number={}, 
pages={2657-2661}, 
keywords={feedforward neural nets;learning (artificial intelligence);recurrent neural nets;speech recognition;batch normalized recurrent neural networks;RNN;sequential data;long-term dependency learning;convergence rate improvement;intermediate representation normalization;feedforward neural networks;speech recognition task;language modeling;training criterion;Training;Recurrent neural networks;Convergence;Speech recognition;Computer architecture;Speech;batch normalization;RNN;LSTM;optimization}, 
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@inproceedings{Saon+2016,
author={George Saon and Tom Sercu and Steven Rennie and Hong-Kwang J. Kuo},
title={The IBM 2016 English Conversational Telephone Speech Recognition System},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-1460},
url={http://dx.doi.org/10.21437/Interspeech.2016-1460},
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@INPROCEEDINGS{Sercu-2016, 
author={T. {Sercu} and C. {Puhrsch} and B. {Kingsbury} and Y. {LeCun}}, 
booktitle={2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
title={Very deep multilingual convolutional neural networks for LVCSR}, 
year={2016}, 
volume={}, 
number={}, 
pages={4955-4959}, 
keywords={natural language processing;neural nets;speech recognition;very deep multilingual convolutional neural networks;LVCSR;CNN;large vocabulary continuous speech recognition systems;word error rate;Training;Context;Hidden Markov models;Neural networks;Computer architecture;Kernel;Training data;Convolutional Networks;Multilingual;Acoustic Modeling;Speech Recognition;Neural Networks}, 
doi={10.1109/ICASSP.2016.7472620}, 
ISSN={2379-190X}, 
month={March},}


@inproceedings{Sercu+2016,
author={Tom Sercu and Vaibhava Goel},
title={Advances in Very Deep Convolutional Neural Networks for LVCSR},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-1033},
url={http://dx.doi.org/10.21437/Interspeech.2016-1033},
pages={3429--3433}
}

@INPROCEEDINGS{Xiong-2018, 
author={W. {Xiong} and L. {Wu} and F. {Alleva} and J. {Droppo} and X. {Huang} and A. {Stolcke}}, 
booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
title={The Microsoft 2017 Conversational Speech Recognition System}, 
year={2018}, 
volume={}, 
number={}, 
pages={5934-5938}, 
keywords={convolution;feedforward neural nets;natural language processing;speaker recognition;speech processing;language model rescoring step;senone level;switchboard domains;character-based LSTM language models;NIST 2000 switchboard test set;frame level;word-level voting;acoustic model posteriors;dialog session aware LSTM language models;CNN-BLSTM acoustic model;Microsoft 2017 conversational speech recognition system;Acoustics;Error analysis;Training;Speech recognition;Switches;Computational modeling;Context modeling;Conversational speech recognition;CNN;LACE;BLSTM;LSTM-LM;system combination;human parity}, 
doi={10.1109/ICASSP.2018.8461870}, 
ISSN={2379-190X}, 
month={April},}

@inproceedings{zeyer2018improved,
  author={Albert Zeyer and Kazuki Irie and Ralf Schlüter and Hermann Ney},
  title={Improved Training of End-to-end Attention Models for Speech Recognition},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={7--11},
  doi={10.21437/Interspeech.2018-1616},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1616}
}

@article{Wav2LetterV2,
  author    = {Vitaliy Liptchinsky and
               Gabriel Synnaeve and
               Ronan Collobert},
  title     = {Letter-Based Speech Recognition with Gated ConvNets},
  journal   = {CoRR},
  volume    = {abs/1712.09444},
  year      = {2017},
  url       = {http://arxiv.org/abs/1712.09444},
  archivePrefix = {arXiv},
  eprint    = {1712.09444},
  timestamp = {Mon, 13 Aug 2018 16:46:33 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1712-09444},
  bibsource = {dblp computer science bibliography, https://dblp.org}
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@article{zeghidour2018,
  author    = {Neil Zeghidour and
               Qiantong Xu and
               Vitaliy Liptchinsky and
               Nicolas Usunier and
               Gabriel Synnaeve and
               Ronan Collobert},
  title     = {Fully Convolutional Speech Recognition},
  journal   = {CoRR},
  volume    = {abs/1812.06864},
  year      = {2018},
  url       = {http://arxiv.org/abs/1812.06864},
  archivePrefix = {arXiv},
  eprint    = {1812.06864},
  timestamp = {Tue, 01 Jan 2019 15:01:25 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1812-06864},
  bibsource = {dblp computer science bibliography, https://dblp.org}
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@inproceedings{Hadian2018,
  author={Hossein Hadian and Hossein Sameti and Daniel Povey and Sanjeev Khudanpur},
  title={End-to-end Speech Recognition Using Lattice-free MMI},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={12--16},
  doi={10.21437/Interspeech.2018-1423},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1423}
}

@inproceedings{Tang2018,
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  booktitle={Proc. Interspeech 2018},
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}

@article{Kurata2017LanguageMW,
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@inproceedings{Saon2017,
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  title={English Conversational Telephone Speech Recognition by Humans and Machines},
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}

@inproceedings{Povey+2016,
author={Daniel Povey and Vijayaditya Peddinti and Daniel Galvez and Pegah Ghahremani and Vimal Manohar and Xingyu Na and Yiming Wang and Sanjeev Khudanpur},
title={Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-595},
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pages={2751--2755}
}

@article{Yang2018,
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               Jiwei Li and
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@article{liptchinsky2017based,
  title={Letter-Based Speech Recognition with Gated ConvNets},
  author={Liptchinsky, Vitaliy and Synnaeve, Gabriel and Collobert, Ronan},
  journal={arXiv preprint arXiv:1712.09444},
  year={2017}
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@inproceedings{Weng2018,
  author={Chao Weng and Jia Cui and Guangsen Wang and Jun Wang and Chengzhu Yu and Dan Su and Dong Yu},
  title={Improving Attention Based Sequence-to-Sequence Models for End-to-End English Conversational Speech Recognition},
  year=2018,
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  doi={10.21437/Interspeech.2018-1030},
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}

@INPROCEEDINGS{Battenberg2017, 
author={E. {Battenberg} and J. {Chen} and R. {Child} and A. {Coates} and Y. G. Y. {Li} and H. {Liu} and S. {Satheesh} and A. {Sriram} and Z. {Zhu}}, 
booktitle={2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)}, 
title={Exploring neural transducers for end-to-end speech recognition}, 
year={2017}, 
volume={}, 
number={}, 
pages={206-213}, 
keywords={recurrent neural nets;speech recognition;Hub500 benchmark;CTC models;speech recognition pipeline;RNN-Transducer models;language model;Seq2Seq models;end-to-end speech recognition;neural transducers;Decoding;Hidden Markov models;Transducers;Task analysis;Speech;Mathematical model;Neural networks}, 
doi={10.1109/ASRU.2017.8268937}, 
ISSN={}, 
month={Dec},
}

@inproceedings{
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booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=Bkg6RiCqY7},
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@article{novograd2019,
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