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Instruction-based speech processing is becoming popular.
V. Kumar, A. Choudhary, and E. Cho, “Data augmentation using pre-trained transformer models,” in Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems , W. M. Campbell, A. Waibel, D. Hakkani-Tur, T. J. Hazen, K. Kilgour, E. Cho, V. Kumar, and H. Glaude, Eds. Suzhou, China: Association for Computational Linguistics, Dec. 2020, pp. 18–26. [Online]. Available: https://aclanthology.org/2020.lifelongnlp-1.3
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J. Wei, M. Bosma, V. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le, “Finetuned language models are zero-shot learners,” in International Conference on Learning Representations , 2021
2021
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K. M. Yoo, D. Park, J. Kang, S.-W. Lee, and W. Park, “GPT3Mix: Leveraging large-scale language models for text augmentation,” in Findings of the Association for Computational Linguistics: EMNLP 2021 , M.-F. Moens, X. Huang, L. Specia, and S. W.-t. Yih, Eds. Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 2225–2239. [Online]. Available: https://aclanthology.org/2021.findings-emnlp.192
2021
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E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al. , “Lora: Low-rank adaptation of large language models,” in International Conference on Learning Representations , 2021
2021
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R. He, L. Liu, H. Ye, Q. Tan, B. Ding, L. Cheng, J. Low, L. Bing, and L. Si, “On the effectiveness of adapter-based tuning for pretrained language model adaptation,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2021, pp. 2208–2222
2021
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J. Wu, Y. Gaur, Z. Chen, L. Zhou, Y. Zhu, T. Wang, J. Li, S. Liu, B. Ren, L. Liu et al. , “On decoder-only architecture for speech-to-text and large language model integration,” in 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2023, pp. 1–8
2023
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M. Wang, W. Han, I. Shafran, Z. Wu, C.-C. Chiu, Y. Cao, N. Chen, Y. Zhang, H. Soltau, P. K. Rubenstein et al. , “Slm: Bridge the thin gap between speech and text foundation models,” in 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2023, pp. 1–8
2023
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Y. Gong, A. H. Liu, H. Luo, L. Karlinsky, and J. Glass, “Joint audio and speech understanding,” in 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) , 2023
2023
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C. Tang, W. Yu, G. Sun, X. Chen, T. Tan, W. Li, L. Lu, M. Zejun, and C. Zhang, “Salmonn: Towards generic hearing abilities for large language models,” in The Twelfth International Conference on Learning Representations , 2023
2023
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2023
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2023
Cited alongside, same era.
Z. Guo, Y. Leng, Y. Wu, S. Zhao, and X. Tan, “Prompttts: Controllable text-to-speech with text descriptions,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Cited alongside, same era.
2023
Cited alongside, same era.
J. Wang, Y. Liang, F. Meng, Z. Sun, H. Shi, Z. Li, J. Xu, J. Qu, and J. Zhou, “Is chatgpt a good nlg evaluator? a preliminary study,” in Proceedings of EMNLP Workshop , 2023, p. 1
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 , 2023, pp. 5178–5193
2023
Later among the works it cites.
Y. Gong, H. Luo, A. H. Liu, L. Karlinsky, and J. R. Glass, “Listen, think, and understand,” in International Conference on Learning Representations , 2024
2024
Closest in time.
E. Nachmani, A. Levkovitch, R. Hirsch, J. Salazar, C. Asawaroengchai, S. Mariooryad, E. Rivlin, R. Skerry-Ryan, and M. T. Ramanovich, “Spoken question answering and speech continuation using spectrogram-powered llm,” in International Conference on Learning Representations , 2024
2024
Closest in time.
Z. Kong, A. Goel, R. Badlani, W. Ping, R. Valle, and B. Catanzaro, “Audio flamingo: A novel audio language model with few-shot learning and dialogue abilities,” in International Conference on Machine Learning , 2024
2024
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Y. Liu, D. Iter, Y. Xu, S. Wang, R. Xu, and C. Zhu, “G-eval: Nlg evaluation using gpt-4 with better human alignment,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , 2023, pp. 2511–2522
2023
Cited alongside, same era.
C.-H. Chiang and H.-Y. Lee, “Can large language models be an alternative to human evaluations?” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2023, pp. 15 607–15 631
2023
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
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” in International conference on machine learning . PMLR, 2023, pp. 19 730–19 742
2023
Cited alongside, same era.
Closest in time.
2024
Closest in time.
K.-H. Lu, Z. Chen, S.-W. Fu, H. Huang, B. Ginsburg, Y.-C. F. Wang, and H. yi Lee, “Desta: Enhancing speech language models through descriptive speech-text alignment,” in Proc. INTERSPEECH 2024 , 2024
2024
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C.-y. Huang, K.-H. Lu, S.-H. Wang, C.-Y. Hsiao, C.-Y. Kuan, H. Wu, S. Arora, K.-W. Chang, J. Shi, Y. Peng et al. , “Dynamic-superb: Towards a dynamic, collaborative, and comprehensive instruction-tuning benchmark for speech,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 12 136–12 140
2024
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Anthropic, “Claude,” https://www.anthropic.com , 2024, large language model
2024
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Y. Lacombe, V. Srivastav, and S. Gandhi, “Data-speech,” https://github.com/ylacombe/dataspeech , 2024
2024
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SoX Contributors, “Sox - sound exchange,” 2024, accessed: 2024-06-16. [Online]. Available: http://sox.sourceforge.net/
2024
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