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The Transformer architecture has been well adopted as a dominant architecture in most sequence transduction tasks including automatic speech recognition (ASR), since its attention mechanism excels in capturing long-range dependencies.
“Speech recognition with deep recurrent neural networks,”
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton, · 2013
Earlier work this paper cites.
“Librispeech: an asr corpus based on public domain audio books,”
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur, · 2015
Earlier work this paper cites.
“Attention is all you need,”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017
Earlier work this paper cites.
“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
Earlier work this paper cites.
“State-of-the-art speech recognition with sequence-to-sequence models,”
Chung-Cheng Chiu, Tara N Sainath, Yonghui Wu, Rohit Prabhavalkar, Patrick Nguyen, Zhifeng Chen, Anjuli Kannan, Ron J Weiss, Kanishka Rao, Ekaterina Gonina, et al., · 2018
Earlier work this paper cites.
“Simple recurrent units for highly parallelizable recurrence,”
Tao Lei, Yu Zhang, Sida I Wang, Hui Dai, and Yoav Artzi, · 2018
Earlier work this paper cites.
“Ted-lium 3: twice as much data and corpus repartition for experiments on speaker adaptation,”
François Hernandez, Vincent Nguyen, Sahar Ghannay, Natalia Tomashenko, and Yannick Esteve, · 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, et al., · 2018
Cited alongside, same era.
“Single headed attention rnn: Stop thinking with your head,”
Stephen Merity, · 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.
“Streaming automatic speech recognition with the transformer model,”
Niko Moritz, Takaaki Hori, and Jonathan Le, · 2020
Later among the works it cites.
“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, et al., · 2020
Later among the works it cites.
“Multistream cnn for robust acoustic modeling,”
Kyu J Han, Jing Pan, Venkata Krishna Naveen Tadala, Tao Ma, and Dan Povey, · 2021
Closest in time.
“When attention meets fast recurrence: Training language models with reduced compute,”
Tao Lei, · 2021
Closest in time.
“Recent developments on espnet toolkit boosted by conformer,”
Pengcheng Guo, Florian Boyer, Xuankai Chang, Tomoki Hayashi, Yosuke Higuchi, Hirofumi Inaguma, Naoyuki Kamo, Chenda Li, Daniel Garcia-Romero, Jiatong Shi, et al., · 2021
Closest in time.
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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.
“Transformer-based acoustic modeling for hybrid speech recognition,”
Yongqiang Wang, Abdelrahman Mohamed, Due Le, Chunxi Liu, Alex Xiao, Jay Mahadeokar, Hongzhao Huang, Andros Tjandra, Xiaohui Zhang, Frank Zhang, et al., · 2020
Cited alongside, same era.