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Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence, and can further reduce the word error rate (WER).
Error correction via a post-processor for continuous speech recognition
Eric Ringger and James Allen. 1996 · 1996
Earlier work this paper cites.
A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (rover)
Jonathan G Fiscus. 1997 · 1997
Earlier work this paper cites.
Improving readability for automatic speech recognition transcription
Junwei Liao, Sefik Emre Eskimez, Liyang Lu, Yu Shi, Ming Gong, Linjun Shou, Hong Qu, and Michael Zeng. 2020 · 2004
Earlier work this paper cites.
Statistical error correction methods for domain-specific asr systems
Horia Cucu, Andi Buzo, Laurent Besacier, and Corneliu Burileanu. 2013 · 2013
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
Earlier work this paper cites.
Automatic correction of asr outputs by using machine translation
Luis Fernando D’Haro and Rafael E Banchs. 2016 · 2016
Earlier work this paper cites.
Highway long short-term memory rnns for distant speech recognition
Yu Zhang, Guoguo Chen, Dong Yu, Kaisheng Yaco, Sanjeev Khudanpur, and James Glass. 2016 · 2016
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 · 2017
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 · 2017
Earlier work this paper cites.
Repairing asr output by artificial development and ontology based learning
C Anantaram, Amit Sangroya, Mrinal Rawat, and Aishwarya Chhabra. 2018 · 2018
Cited alongside, same era.
SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Taku Kudo and John Richardson. 2018 · 2018
Cited alongside, same era.
Deterministic non-autoregressive neural sequence modeling by iterative refinement
Jason Lee, Elman Mansimov, and Kyunghyun Cho. 2018 · 2018
Cited alongside, same era.
Neural error corrective language models for automatic speech recognition
Tomohiro Tanaka, Ryo Masumura, Hirokazu Masataki, and Yushi Aono. 2018 · 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 · 2018
Cited alongside, same era.
Learning from past mistakes: improving automatic speech recognition output via noisy-clean phrase context modeling
Prashanth Gurunath Shivakumar, Haoqi Li, Kevin Knight, and Panayiotis Georgiou. 2019 · 2019
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 · 2020
Later among the works it cites.
Fine-tuning by curriculum learning for non-autoregressive neural machine translation
Junliang Guo, Xu Tan, Linli Xu, Tao Qin, Enhong Chen, and Tie-Yan Liu. 2020 · 2020
Later among the works it cites.
Asr error correction and domain adaptation using machine translation
Anirudh Mani, Shruti Palaskar, Nimshi Venkat Meripo, Sandeep Konam, and Florian Metze. 2020 · 2020
Later among the works it cites.
End-to-end automatic speech recognition: Its impact on the workflowin documenting yoloxóchitl mixtec
Jonathan D Amith, Jiatong Shi, and Rey Castillo García. 2021 · 2021
Closest in time.
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Levenshtein transformer
Jiatao Gu, Changhan Wang, and Junbo Zhao. 2019 · 2019
Cited alongside, same era.
Non-autoregressive neural machine translation with enhanced decoder input
Junliang Guo, Xu Tan, Di He, Tao Qin, Linli Xu, and Tie-Yan Liu. 2019 · 2019
Cited alongside, same era.
fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 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 · 2019
Cited alongside, same era.
Fastcorrect: Fast error correction with edit alignment for automatic speech recognition
Yichong Leng, Xu Tan, Linchen Zhu, Jin Xu, Renqian Luo, Linquan Liu, Tao Qin, Xiang-Yang Li, Ed Lin, and Tie-Yan Liu. 2021 · 2021
Closest in time.
Asr n-best fusion nets
Xinyue Liu, Mingda Li, Luoxin Chen, Prashan Wanigasekara, Weitong Ruan, Haidar Khan, Wael Hamza, and Chengwei Su. 2021 · 2021
Closest in time.
Timo Lohrenz, Zhengyang Li, and Tim Fingscheidt. 2021 · 2021
Closest in time.