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From wearables to powerful smart devices, modern automatic speech recognition (ASR) models run on a variety of edge devices with different computational budgets.
“Sequence transduction with recurrent neural networks,”
Alex Graves, · 2012
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.
“Audio augmentation for speech recognition,”
Tom Ko, Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur, · 2015
Earlier work this paper cites.
“Block-sparse recurrent neural networks,”
Sharan Narang, Eric Undersander, and Gregory Diamos, · 2017
Earlier work this paper cites.
“Slimmable neural networks,”
Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, and Thomas Huang, · 2018
Earlier work this paper cites.
“Snip: Single-shot network pruning based on connection sensitivity,”
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr, · 2018
Earlier work this paper cites.
“To prune, or not to prune: Exploring the efficacy of pruning for model compression,”
Michael Zhu and Suyog Gupta, · 2018
Earlier work this paper cites.
“Streaming end-to-end speech recognition for mobile devices,”
Yanzhang He, Tara N Sainath, Rohit Prabhavalkar, Ian McGraw, Raziel Alvarez, Ding Zhao, David Rybach, Anjuli Kannan, Yonghui Wu, Ruoming Pang, et al., · 2019
Earlier work this paper cites.
“Universally slimmable networks and improved training techniques,”
Jiahui Yu and Thomas S Huang, · 2019
Cited alongside, same era.
“Optimizing speech recognition for the edge,”
Yuan Shangguan, Jian Li, Qiao Liang, Raziel Alvarez, and Ian McGraw, · 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.
“A review of on-device fully neural end-to-end automatic speech recognition algorithms,”
Chanwoo Kim, Dhananjaya Gowda, Dongsoo Lee, Jiyeon Kim, Ankur Kumar, Sungsoo Kim, Abhinav Garg, and Changwoo Han, · 2020
Cited alongside, same era.
“Bignas: Scaling up neural architecture search with big single-stage models,”
Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, and Quoc Le, · 2020
Cited alongside, same era.
“Collaborative training of acoustic encoders for speech recognition,”
Varun Nagaraja, Yangyang Shi, Ganesh Venkatesh, Ozlem Kalinli, Michael L Seltzer, and Vikas Chandra, · 2021
Closest in time.
“Simultaneous training of partially masked neural networks,”
Amirkeivan Mohtashami, Martin Jaggi, and Sebastian U Stich, · 2021
Closest in time.
“Dynamic sparsity neural networks for automatic speech recognition,”
Zhaofeng Wu, Ding Zhao, Qiao Liang, Jiahui Yu, Anmol Gulati, and Ruoming Pang, · 2021
Closest in time.
“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
Closest in time.
“Alphanet: Improved training of supernet with alpha-divergence,”
Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, and Vikas Chandra, · 2021
Closest in time.
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“Once-for-all: Train one network and specialize it for efficient deployment,”
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han, · 2020
Cited alongside, same era.
“Hat: Hardware-aware transformers for efficient natural language processing,”
Hanrui Wang, Zhanghao Wu, Zhijian Liu, Han Cai, Ligeng Zhu, Chuang Gan, and Song Han, · 2020
Cited alongside, same era.
“Extremely low footprint end-to-end asr system for smart device,”
Zhifu Gao, Yiwu Yao, Shiliang Zhang, Jun Yang, Ming Lei, and Ian McLoughlin, · 2021
Closest in time.
“Emformer: Efficient memory transformer based acoustic model for low latency streaming speech recognition,”
Yangyang Shi, Yongqiang Wang, Chunyang Wu, Ching-Feng Yeh, Julian Chan, Frank Zhang, Duc Le, and Mike Seltzer, · 2021
Closest in time.