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Recent studies have shown that neural vocoders based on generative adversarial network (GAN) can generate audios with high quality.
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2018
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Y. Luo and N. Mesgarani, “Tasnet: Time-domain audio separation network for real-time, single-channel speech separation,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2018, pp. 696–700
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2018
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R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow-based generative network for speech synthesis,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 3617–3621
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J. Valin and J. Skoglund, “Lpcnet: Improving neural speech synthesis through linear prediction,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 5891–5895
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2020
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R. Yamamoto, E. Song, and J. M. Kim, “Parallel wavegan: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2020, pp. 6199–6203
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G. Yang, S. Yang, K. Liu, P. Fang, W. Chen, and L. Xie, “Multi-band melgan: Faster waveform generation for high-quality text-to-speech,” 2020
2020
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W. Jang, D. Lim, and J. Yoon, “Universal melgan: A robust neural vocoder for high-fidelity waveform generation in multiple domains,” 2021
2021
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