Fetching the paper…
Reading the bibliography…
Pre-trained multilingual speech foundation models, like Whisper, have shown impressive performance across different languages.
A. Roze, S. Yin, Z. Zhang, D. Wang, and A. Hamdulla, “Thugy20: A free uyghur speech database,” in NCMMSC’15 , 2015
2015
Earlier work this paper cites.
F. Zenke, B. Poole, and S. Ganguli, “Continual learning through synaptic intelligence,” in International conference on machine learning . PMLR, 2017, pp. 3987–3995
2017
Earlier work this paper cites.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska et al. , “Overcoming catastrophic forgetting in neural networks,” Proceedings of the national academy of sciences , vol. 114, no. 13, pp. 3521–3526, 2017
2017
Earlier work this paper cites.
H. Bu, J. Du, X. Na, B. Wu, and H. Zheng, “Aishell-1: An open-source mandarin speech corpus and a speech recognition baseline,” in 2017 20th conference of the oriental chapter of the international coordinating committee on speech databases and speech I/O systems and assessment (O-COCOSDA) . IEEE, 2017, pp. 1–5
2017
Earlier work this paper cites.
R. Aljundi, F. Babiloni, M. Elhoseiny, M. Rohrbach, and T. Tuytelaars, “Memory aware synapses: Learning what (not) to forget,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 139–154
2018
Earlier work this paper cites.
U. Hermjakob, J. May, and K. Knight, “Out-of-the-box universal Romanization tool uroman,” in Proceedings of ACL 2018, System Demonstrations , F. Liu and T. Solorio, Eds. Melbourne, Australia: Association for Computational Linguistics, Jul. 2018, pp. 13–18
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
D. Rolnick, A. Ahuja, J. Schwarz, T. Lillicrap, and G. Wayne, “Experience replay for continual learning,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
M. Farajtabar, N. Azizan, A. Mott, and A. Li, “Orthogonal gradient descent for continual learning,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2020, pp. 3762–3773
2020
Earlier work this paper cites.
R. Ardila, M. Branson, K. Davis, M. Kohler, J. Meyer, M. Henretty, R. Morais, L. Saunders, F. M. Tyers, and G. Weber, “Common voice: A massively-multilingual speech corpus,” in Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11-16, 2020 , N. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, and S. Piperidis, Eds. European Language Resources Association, 2020, pp. 4218–4222. [Online]. Available: https://aclanthology.org/2020.lrec-1.520/
2020
Earlier work this paper cites.
Y. Zhao, X. Xu, J. Yue, W. Song, X. Li, L. Wu, and Q. Ji, “An open speech resource for tibetan multi-dialect and multitask recognition,” International Journal of Computational Science and Engineering , vol. 22, no. 2-3, pp. 297–304, 2020
2020
Cited alongside, same era.
W.-N. Hsu, B. Bolte, Y.-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed, “Hubert: Self-supervised speech representation learning by masked prediction of hidden units,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 29, pp. 3451–3460, 2021
2021
Cited alongside, same era.
H. Chang, H. Lee, and L. Lee, “Towards lifelong learning of end-to-end ASR,” in Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association, Brno, Czechia, 30 August - 3 September 2021 , H. Hermansky, H. Cernocký, L. Burget, L. Lamel, O. Scharenborg, and P. Motlícek, Eds. ISCA, 2021, pp. 2551–2555
2021
Cited alongside, same era.
A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” in International Conference on Machine Learning . PMLR, 2023, pp. 28 492–28 518
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Wang, Y. Liu, T. Ji, X. Wang, Y. Wu, C. Jiang, Y. Chao, Z. Han, L. Wang, X. Shao et al. , “Rehearsal-free continual language learning via efficient parameter isolation,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2023, pp. 10 933–10 946
2023
Later among the works it cites.
A. Razdaibiedina, Y. Mao, R. Hou, M. Khabsa, M. Lewis, and A. Almahairi, “Progressive prompts: Continual learning for language models,” in The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 , 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “LoRA: Low-rank adaptation of large language models,” in International Conference on Learning Representations , 2022. [Online]. Available: https://openreview.net/forum?id=nZeVKeeFYf9
2022
Cited alongside, same era.
M. Yang, I. R. Lane, and S. Watanabe, “Online continual learning of end-to-end speech recognition models,” in Interspeech 2022, 23rd Annual Conference of the International Speech Communication Association, Incheon, Korea, 18-22 September 2022 , H. Ko and J. H. L. Hansen, Eds. ISCA, 2022, pp. 2668–2672
2022
Cited alongside, same era.
B. Zhang, H. Lv, P. Guo, Q. Shao, C. Yang, L. Xie, X. Xu, H. Bu, X. Chen, C. Zeng et al. , “Wenetspeech: A 10000+ hours multi-domain mandarin corpus for speech recognition,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 6182–6186
2022
Cited alongside, same era.
J. N. Senyan Li, Guanyu Li, “XBMU-AMDO31:An open source of Amdo Tibetan speech database and speech recognition baseline system,” in National Conference on Man-Machine Speech Communication,NCMMSC2022 , 2022
2022
Cited alongside, same era.
S. Vander Eeckt and H. Van Hamme, “Continual learning for monolingual end-to-end automatic speech recognition,” in 2022 30th European Signal Processing Conference (EUSIPCO) . IEEE, 2022, pp. 459–463
2022
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
S. Vander Eeckt and H. Van Hamme, “Using adapters to overcome catastrophic forgetting in end-to-end automatic speech recognition,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Later among the works it cites.
X. Wang, T. Chen, Q. Ge, H. Xia, R. Bao, R. Zheng, Q. Zhang, T. Gui, and X. Huang, “Orthogonal subspace learning for language model continual learning,” in Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023 , H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, 2023, pp. 10 658–10 671
2023
Later among the works it cites.
Q. Zhang, M. Chen, A. Bukharin, P. He, Y. Cheng, W. Chen, and T. Zhao, “Adaptive budget allocation for parameter-efficient fine-tuning,” in The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
G. I. Winata, L. Xie, K. Radhakrishnan, S. Wu, X. Jin, P. Cheng, M. Kulkarni, and D. Preotiuc-Pietro, “Overcoming catastrophic forgetting in massively multilingual continual learning,” in Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023 , A. Rogers, J. L. Boyd-Graber, and N. Okazaki, Eds. Association for Computational Linguistics, 2023, pp. 768–777
2023
Later among the works it cites.