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Whisper is a powerful automatic speech recognition (ASR) model.
“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.
“Parameter-efficient transfer learning for nlp,”
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly, · 2019
Earlier work this paper cites.
“wav2vec 2.0: A framework for self-supervised learning of speech representations,”
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli, · 2020
Earlier work this paper cites.
“Applying wav2vec2. 0 to speech recognition in various low-resource languages,”
Cheng Yi, Jianzhong Wang, Ning Cheng, Shiyu Zhou, and Bo Xu, · 2020
Earlier work this paper cites.
“Common voice: A massively-multilingual speech corpus,”
Rosana Ardila, Megan Branson, Kelly Davis, Michael Kohler, Josh Meyer, Michael Henretty, Reuben Morais, Lindsay Saunders, Francis M. Tyers, and Gregor Weber, · 2020
Earlier work this paper cites.
“Development of the cuhk elderly speech recognition system for neurocognitive disorder detection using the dementiabank corpus,”
Zi Ye, Shoukang Hu, Jinchao Li, Xurong Xie, Mengzhe Geng, Jianwei Yu, Junhao Xu, Boyang Xue, Shansong Liu, Xunying Liu, et al., · 2021
Earlier work this paper cites.
“The power of scale for parameter-efficient prompt tuning,”
Brian Lester, Rami Al-Rfou, and Noah Constant, · 2021
Earlier work this paper cites.
“Prefix-tuning: Optimizing continuous prompts for generation,”
Xiang Lisa Li and Percy Liang, · 2021
Earlier work this paper cites.
“Intrinsic dimensionality explains the effectiveness of language model fine-tuning,”
Armen Aghajanyan, Sonal Gupta, and Luke Zettlemoyer, · 2021
Cited alongside, same era.
“The slt 2021 children speech recognition challenge: Open datasets, rules and baselines,”
Fan Yu, Zhuoyuan Yao, Xiong Wang, Keyu An, Lei Xie, Zhijian Ou, Bo Liu, Xiulin Li, and Guanqiong Miao, · 2021
Cited alongside, same era.
“XLS-R: self-supervised cross-lingual speech representation learning at scale,”
Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, and Michael Auli, · 2022
Cited alongside, same era.
“End-to-end neural systems for automatic children speech recognition: An empirical study,”
Prashanth Gurunath Shivakumar and Shrikanth Narayanan, · 2022
Cited alongside, same era.
“Modular and parameter-efficient fine-tuning for nlp models,”
Sebastian Ruder, Jonas Pfeiffer, and Ivan Vulić, · 2022
Cited alongside, same era.
“Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,”
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel, · 2022
Later among the works it cites.
“Platon: Pruning large transformer models with upper confidence bound of weight importance,”
Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, and Tuo Zhao, · 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.
“Google USM: Scaling automatic speech recognition beyond 100 languages,”
Yu Zhang, Wei Han, James Qin, Yongqiang Wang, Ankur Bapna, Zhehuai Chen, Nanxin Chen, Bo Li, Vera Axelrod, Gary Wang, et al., · 2023
Closest in time.
“Scaling speech technology to 1,000+ languages,”
Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, et al., · 2023
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“Towards a unified view of parameter-efficient transfer learning,”
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig, · 2022
Cited alongside, same era.
“Lora: Low-rank adaptation of large language models,”
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen, · 2022
Cited alongside, same era.
“BitFit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,”
Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel, · 2022
Cited alongside, same era.
Closest in time.
“Parameter-efficient fine-tuning of large-scale pre-trained language models,”
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al., · 2023
Closest in time.
“Adaptive budget allocation for parameter-efficient fine-tuning,”
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao, · 2023
Closest in time.
“One-for-all: Generalized lora for parameter-efficient fine-tuning,”
Arnav Chavan, Zhuang Liu, Deepak Gupta, Eric Xing, and Zhiqiang Shen, · 2023
Closest in time.