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Recently, the application of diffusion models has facilitated the significant development of speech and audio generation.
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B. Elizalde, S. Deshmukh, M. Al Ismail, and H. Wang, “Clap: learning audio concepts from natural language supervision,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing , 2023, pp. 1–5
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2023
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D. Ghosal, N. Majumder, A. Mehrish, and S. Poria, “Text-to-audio generation using instruction guided latent diffusion model,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023, pp. 3590–3598
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2023
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2023
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2024
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W. Guan, Y. Li, T. Li, H. Huang, F. Wang, J. Lin, L. Huang, L. Li, and Q. Hong, “Mm-tts: Multi-modal prompt based style transfer for expressive text-to-speech synthesis,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 16, 2024, pp. 18 117–18 125
2024
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X. Tan, J. Chen, H. Liu, J. Cong, C. Zhang, Y. Liu, X. Wang, Y. Leng, Y. Yi, L. He et al. , “Naturalspeech: End-to-end text-to-speech synthesis with human-level quality,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
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2024
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W. Guan, Q. Su, H. Zhou, S. Miao, X. Xie, L. Li, and Q. Hong, “Reflow-tts: A rectified flow model for high-fidelity text-to-speech,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 10 501–10 505
2024
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