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Large-scale training corpora have significantly improved the performance of ASR models.
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S. Chen, C. Wang, Z. Chen, Y. Wu, S. Liu, Z. Chen, J. Li, N. Kanda, T. Yoshioka, X. Xiao et al. , “Wavlm: Large-scale self-supervised pre-training for full stack speech processing,” IEEE Journal of Selected Topics in Signal Processing , vol. 16, no. 6, pp. 1505–1518, 2022
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B. Zhang, D. Wu, Z. Peng, X. Song, Z. Yao, H. Lv, L. Xie, C. Yang, F. Pan, and J. Niu, “WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit,” in Interspeech , 2022
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A. Baevski, W.-N. Hsu, Q. Xu, A. Babu, J. Gu, and M. Auli, “Data2vec: A general framework for self-supervised learning in speech, vision and language,” in International Conference on Machine Learning . PMLR, 2022, pp. 1298–1312
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B. Zhang, H. Lv, P. Guo, Q. Shao, C. Yang, L. Xie, X. Xu, H. Bu, X. Chen, C. Zeng, D. Wu, and Z. Peng, “WENETSPEECH: A 10000+ Hours Multi-Domain Mandarin Corpus for Speech Recognition,” in ICASSP , 2022
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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 ICLR , 2022
2022
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2023
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2023
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Z. Yao, L. Guo, X. Yang, W. Kang, F. Kuang, Y. Yang, Z. Jin, L. Lin, and D. Povey, “Zipformer: A faster and better encoder for automatic speech recognition,” in The Twelfth International Conference on Learning Representations , 2023
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V. Pratap, A. Tjandra, B. Shi, P. Tomasello, A. Babu, S. Kundu, A. Elkahky, Z. Ni, A. Vyas, M. Fazel-Zarandi et al. , “Scaling speech technology to 1,000+ languages,” Journal of Machine Learning Research , vol. 25, no. 97, pp. 1–52, 2024
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R. Huang, M. Li, D. Yang, J. Shi, X. Chang, Z. Ye, Y. Wu, Z. Hong, J. Huang, J. Liu et al. , “Audiogpt: Understanding and generating speech, music, sound, and talking head,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 21, 2024, pp. 23 802–23 804
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Z. Gao, Z. Li, J. Wang, H. Luo, X. Shi, M. Chen, Y. Li, L. Zuo, Z. Du, and S. Zhang, “Funasr: A fundamental end-to-end speech recognition toolkit,” in Interspeech 2023 , 2023, pp. 1593–1597
2023
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2023
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OpenAI, “GPT-4 Technical Report,” CoRR , 2023
2023
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2023
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C. Tang, W. Yu, G. Sun, X. Chen, T. Tan, W. Li, L. Lu, Z. Ma, and C. Zhang, “SALMONN: Towards Generic Hearing Abilities for Large Language Models,” CoRR , 2023
2023
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Y. Chu, J. Xu, X. Zhou, Q. Yang, S. Zhang, Z. Yan, C. Zhou, and J. Zhou, “Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models,” CoRR , 2023
2023
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S. Chen, Y. Wu, C. Wang, S. Liu, D. Tompkins, Z. Chen, W. Che, X. Yu, and F. Wei, “BEATs: Audio pre-training with acoustic tokenizers,” in Proceedings of the 40th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, Eds., vol. 202. PMLR, 23–29 Jul 2023, pp. 5178–5193. [Online]. Available: https://proceedings.mlr.press/v202/chen23ag.html
2023
Cited alongside, same era.
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2024
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Z. Ma, G. Yang, Y. Yang, Z. Gao, J. Wang, Z. Du, F. Yu, Q. Chen, S. Zheng, S. Zhang, and X. Chen, “An Embarrassingly Simple Approach for LLM with Strong ASR Capacity,” CoRR , 2024
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2024
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X. Geng, T. Xu, K. Wei, B. Mu, H. Xue, H. Wang, Y. Li, P. Guo, Y. Dai, L. Li et al. , “Unveiling the potential of llm-based asr on chinese open-source datasets,” in 2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP) . IEEE, 2024, pp. 26–30
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2024
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H. Chen, Z. Li, G. Xia, B. Liu, Y. Yang, J. Kang, and J. Li, “Telespeechpt: Large-scale chinese multi-dialect and multi-accent speech pre-training,” in National Conference on Man-Machine Speech Communication . Springer, 2024, pp. 183–190
2024
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W. Yu, C. Tang, G. Sun, X. Chen, T. Tan, W. Li, L. Lu, Z. Ma, and C. Zhang, “Connecting speech encoder and large language model for asr,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 12 637–12 641
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