Generative error correction for code-switching speech recognition using large language models

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2013

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H. Adel, N. T. Vu, F. Kraus, T. Schlippe, H. Li, and T. Schultz, “Recurrent neural network language modeling for code switching conversational speech,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing . IEEE, 2013, pp. 8411–8415

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2016

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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 . IEEE, 2017, pp. 1–5

2017

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N. Luo, D. Jiang, S. Zhao, C. Gong, W. Zou, and X. Li, “Towards end-to-end code-switching speech recognition,” arXiv preprint arXiv:1810.13091 , 2018

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2018

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K. Li, J. Li, G. Ye, R. Zhao, and Y. Gong, “Towards code-switching asr for end-to-end ctc models,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 6076–6080

2019

Cited alongside, same era.

X. Yue, G. Lee, E. Yılmaz, F. Deng, and H. Li, “End-to-end code-switching asr for low-resourced language pairs,” in 2019 IEEE ASRU . IEEE, 2019, pp. 972–979

2019

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X. Zhou, X. Tian, G. Lee, R. K. Das, and H. Li, “End-to-end code-switching tts with cross-lingual language model,” in ICASSP . IEEE, 2020, pp. 7614–7618

2020

Cited alongside, same era.

X. Shi, Q. Feng, and L. Xie, “The asru 2019 mandarin-english code-switching speech recognition challenge: Open datasets, tracks, methods and results,” arXiv preprint arXiv:2007.05916 , 2020

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2020

Cited alongside, same era.

Y. Peng, Y. Liu, J. Zhang, H. Xu, Y. He, H. Huang, and E. S. Chng, “Internal language model estimation based language model fusion for cross-domain code-switching speech recognition,” arXiv preprint arXiv:2207.04176 , 2022

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2022

Later among the works it cites.

H.-W. Wang, B.-C. Yan, Y.-C. Wang, and B. Chen, “Effective asr error correction leveraging phonetic, semantic information and n-best hypotheses,” in 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) . IEEE, 2022, pp. 117–122

2022

Later among the works it cites.

H. Liu, H. Xu, L. P. Garcia, A. W. H. Khong, Y. He, and S. Khudanpur, “Reducing language confusion for code-switching speech recognition with token-level language diarization,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. , 2023, pp. 1–5

2023

Closest in time.

C. Chen, Y. Hu, C.-H. H. Yang, S. M. Siniscalchi, P.-Y. Chen, and E. S. Chng, “Hyporadise: An open baseline for generative speech recognition with large language models,” arXiv preprint arXiv:2309.15701 , 2023