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ASR error correction is an interesting option for post processing speech recognition system outputs.
“A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (ROVER),”
Jonathan G Fiscus, · 1997
Earlier work this paper cites.
“Speech recognition in the human–computer interface,”
Carl M Rebman Jr, Milam W Aiken, and Casey G Cegielski, · 2003
Earlier work this paper cites.
“Statistical error correction methods for domain-specific ASR systems,”
Horia Cucu, Andi Buzo, Laurent Besacier, and Corneliu Burileanu, · 2013
Earlier work this paper cites.
“Towards end-to-end speech recognition with recurrent neural networks,”
Alex Graves and Navdeep Jaitly, · 2014
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.
“Listen, Attend and Spell: A neural network for large vocabulary conversational speech recognition,”
William Chan, Navdeep Jaitly, Quoc Le, and Oriol Vinyals, · 2016
Earlier work this paper cites.
“Deep Speech 2: End-to-end speech recognition in English and Mandarin,”
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al., · 2016
Earlier work this paper cites.
“Automatic speech recognition errors detection and correction: A review,”
Rahhal Errattahi, Asmaa El Hannani, and Hassan Ouahmane, · 2018
Earlier work this paper cites.
“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
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
Earlier work this paper cites.
“A spelling correction model for end-to-end speech recognition,”
Jinxi Guo, Tara N Sainath, and Ron J Weiss, · 2019
Earlier work this paper cites.
“BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,”
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, · 2019
Earlier work this paper cites.
“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.
“ASR error correction with augmented transformer for entity retrieval,”
Haoyu Wang, Shuyan Dong, Yue Liu, James Logan, Ashish Kumar Agrawal, and Yang Liu, · 2020
Cited alongside, same era.
“Correction of automatic speech recognition with transformer sequence-to-sequence model,”
Oleksii Hrinchuk, Mariya Popova, and Boris Ginsburg, · 2020
Cited alongside, same era.
“Neural lattice search for speech recognition,”
Rao Ma, Hao Li, Qi Liu, Lu Chen, and Kai Yu, · 2020
Cited alongside, same era.
“Exploring the limits of transfer learning with a unified text-to-text transformer,”
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu, · 2020
Cited alongside, same era.
“N-best ASR transformer: Enhancing SLU performance using multiple ASR hypotheses,”
Karthik Ganesan, Pakhi Bamdev, B Jaivarsan, Amresh Venugopal, and Abhinav Tushar, · 2021
Later among the works it cites.
“Training language models to follow instructions with human feedback,”
Long Ouyang, Jeffrey Wu, Xu Jiang, Almeida, et al., · 2022
Later among the works it cites.
“Robust speech recognition via large-scale weak supervision,”
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever, · 2023
Closest in time.
Rao Ma, Mark JF Gales, Kate Knill, and Mengjie Qian, · 2023
Closest in time.
“Adapting an Unadaptable ASR System,”
Rao Ma, Mengjie Qian, Mark JF Gales, and Kate M Knill, · 2023
Closest in time.
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“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
Cited alongside, same era.
“Artie Bias Corpus: An open dataset for detecting demographic bias in speech applications,”
Josh Meyer, Lindy Rauchenstein, Joshua D Eisenberg, and Nicholas Howell, · 2020
Cited alongside, same era.
“Common Voice: A Massively-Multilingual Speech Corpus,”
Rosana Ardila, Megan Branson, Kelly Davis, Michael Kohler, Josh Meyer, Michael Henretty, Reuben Morais, Lindsay Saunders, Francis Tyers, and Gregor Weber, · 2020
Cited alongside, same era.
“Internal language model estimation for domain-adaptive end-to-end speech recognition,”
Zhong Meng, Sarangarajan Parthasarathy, Eric Sun, Yashesh Gaur, Naoyuki Kanda, Liang Lu, Xie Chen, Rui Zhao, Jinyu Li, and Yifan Gong, · 2021
Cited alongside, same era.
“FastCorrect 2: Fast error correction on multiple candidates for automatic speech recognition,”
Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, et al., · 2021
Cited alongside, same era.
“Improving ASR error correction using N-best hypotheses,”
Linchen Zhu, Wenjie Liu, Linquan Liu, and Edward Lin, · 2021
Cited alongside, same era.
“ASR N-best fusion nets,”
Xinyue Liu, Mingda Li, Luoxin Chen, Prashan Wanigasekara, Weitong Ruan, Haidar Khan, Wael Hamza, and Chengwei Su, · 2021
Cited alongside, same era.
“GPT-4 technical report,” 2023
OpenAI, · 2023
Closest in time.
“Llama: Open and efficient foundation language models,”
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al., · 2023
Closest in time.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al., · 2023
Closest in time.
“Summary of ChatGPT/GPT-4 research and perspective towards the future of large language models,”
Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, et al., · 2023
Closest in time.
“ChatGPT as a factual inconsistency evaluator for abstractive text summarization,”
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou, · 2023
Closest in time.
“ChatGPT or Grammarly? evaluating ChatGPT on grammatical error correction benchmark,”
Haoran Wu, Wenxuan Wang, Yuxuan Wan, Wenxiang Jiao, and Michael Lyu, · 2023
Closest in time.
“Is ChatGPT a highly fluent grammatical error correction system? a comprehensive evaluation,”
Tao Fang, Shu Yang, Kaixin Lan, Derek F Wong, Jinpeng Hu, Lidia S Chao, and Yue Zhang, · 2023
Closest in time.