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The Conformer has become the most popular encoder model for automatic speech recognition (ASR).
Faster, simpler and more accurate hybrid asr systems using wordpieces
Frank Zhang, Yongqiang Wang, Xiaohui Zhang, Chunxi Liu, Yatharth Saraf, and Geoffrey Zweig · 2005
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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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Sequence transduction with recurrent neural networks
Alex Graves · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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William Chan, Navdeep Jaitly, Quoc V Le, and Oriol Vinyals · 2015
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Audio augmentation for speech recognition
Tom Ko, Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur · 2015
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Librispeech: an asr corpus based on public domain audio books
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
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Aishell-1: An open-source mandarin speech corpus and a speech recognition baseline
Hui Bu, Jiayu Du, Xingyu Na, Bengu Wu, and Hao Zheng · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Hybrid ctc/attention architecture for end-to-end speech recognition
Shinji Watanabe, Takaaki Hori, Suyoun Kim, John R Hershey, and Tomoki Hayashi · 2017
Cited alongside, same era.
Towards end-to-end speech recognition with deep convolutional neural networks
Ying Zhang, Mohammad Pezeshki, Philémon Brakel, Saizheng Zhang, Cesar Laurent Yoshua Bengio, and Aaron Courville · 2017
Cited alongside, same era.
Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition
Linhao Dong, Shuang Xu, and Bo Xu · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
ESPnet: End-to-end speech processing toolkit
Shinji Watanabe, Takaaki Hori, Shigeki Karita, Tomoki Hayashi, Jiro Nishitoba, Yuya Unno, Nelson Enrique Yalta Soplin, Jahn Heymann, Matthew Wiesner, Nanxin Chen, Adithya Renduchintala, and Tsubasa Ochiai · 2018
Cited alongside, same era.
Alignment restricted streaming recurrent neural network transducer
Jay Mahadeokar, Yuan Shangguan, Duc Le, Gil Keren, Hang Su, Thong Le, Ching-Feng Yeh, Christian Fuegen, and Michael L Seltzer · 2021
Later among the works it cites.
WeNet: Production Oriented Streaming and Non-Streaming End-to-End Speech Recognition Toolkit
Zhuoyuan Yao, Di Wu, Xiong Wang, Binbin Zhang, Fan Yu, Chao Yang, Zhendong Peng, Xiaoyu Chen, Lei Xie, and Xin Lei · 2021
Later among the works it cites.
Lhotse: a speech data representation library for the modern deep learning ecosystem
Piotr Żelasko, Daniel Povey, Jan Trmal, Sanjeev Khudanpur, et al · 2021
Later among the works it cites.
Nextformer: A convnext augmented conformer for end-to-end speech recognition
Yongjun Jiang, Jian Yu, Wenwen Yang, Bihong Zhang, and Yanfeng Wang · 2022
Later among the works it cites.
Squeezeformer: An efficient transformer for automatic speech recognition
Sehoon Kim, Amir Gholami, Albert Shaw, Nicholas Lee, Karttikeya Mangalam, Jitendra Malik, Michael W Mahoney, and Kurt Keutzer · 2022
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A comparative study on transformer vs rnn in speech applications
Shigeki Karita, Nanxin Chen, Tomoki Hayashi, Takaaki Hori, Hirofumi Inaguma, Ziyan Jiang, Masao Someki, Nelson Enrique Yalta Soplin, Ryuichi Yamamoto, Xiaofei Wang, et al · 2019
Cited alongside, same era.
Nemo: a toolkit for building ai applications using neural modules
Oleksii Kuchaiev, Jason Li, Huyen Nguyen, Oleksii Hrinchuk, Ryan Leary, Boris Ginsburg, Samuel Kriman, Stanislav Beliaev, Vitaly Lavrukhin, Jack Cook, et al · 2019
Cited alongside, same era.
Jasper: An end-to-end convolutional neural acoustic model
Jason Li, Vitaly Lavrukhin, Boris Ginsburg, Ryan Leary, Oleksii Kuchaiev, Jonathan M Cohen, Huyen Nguyen, and Ravi Teja Gadde · 2019
Cited alongside, same era.
Specaugment: A simple data augmentation method for automatic speech recognition
Daniel S Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D Cubuk, and Quoc V Le · 2019
Cited alongside, same era.
Conformer: Convolution-augmented Transformer for Speech Recognition
Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, and Ruoming Pang · 2020
Cited alongside, same era.
Wei Han, Zhengdong Zhang, Yu Zhang, Jiahui Yu, Chung-Cheng Chiu, James Qin, Anmol Gulati, Ruoming Pang, and Yonghui Wu · 2020
Cited alongside, same era.
Quartznet: Deep automatic speech recognition with 1d time-channel separable convolutions
Samuel Kriman, Stanislav Beliaev, Boris Ginsburg, Jocelyn Huang, Oleksii Kuchaiev, Vitaly Lavrukhin, Ryan Leary, Jason Li, and Yang Zhang · 2020
Cited alongside, same era.
Later among the works it cites.
Pruned rnn-t for fast, memory-efficient asr training
Fangjun Kuang, Liyong Guo, Wei Kang, Long Lin, Mingshuang Luo, Zengwei Yao, and Daniel Povey · 2022
Later among the works it cites.
A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
Later among the works it cites.
Branchformer: Parallel mlp-attention architectures to capture local and global context for speech recognition and understanding
Yifan Peng, Siddharth Dalmia, Ian Lane, and Shinji Watanabe · 2022
Later among the works it cites.
3m: Multi-loss, multi-path and multi-level neural networks for speech recognition
Zhao You, Shulin Feng, Dan Su, and Dong Yu · 2022
Later among the works it cites.
Fast and parallel decoding for transducer
Wei Kang, Liyong Guo, Fangjun Kuang, Long Lin, Mingshuang Luo, Zengwei Yao, Xiaoyu Yang, Piotr Żelasko, and Daniel Povey · 2023
Closest in time.
E-branchformer: Branchformer with enhanced merging for speech recognition
Kwangyoun Kim, Felix Wu, Yifan Peng, Jing Pan, Prashant Sridhar, Kyu J Han, and Shinji Watanabe · 2023
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Structured state space decoder for speech recognition and synthesis
Koichi Miyazaki, Masato Murata, and Tomoki Koriyama · 2023
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Accelerating rnn-t training and inference using ctc guidance
Yongqiang Wang, Zhehuai Chen, Chengjian Zheng, Yu Zhang, Wei Han, and Parisa Haghani · 2023
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