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Recent developments in generative models have shown that deep learning combined with traditional digital signal processing (DSP) techniques could successfully generate convincing violin samples [1], that source-excitation combined with WaveNet yields high-quality vocoders [2, 3] and that generative adversarial network (GAN) training can improve naturalness [4, 5].
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A. van den Oord, Y. Li, I. Babuschkin, K. Simonyan, O. Vinyals, K. Kavukcuoglu, G. van den Driessche, E. Lockhart, L. C. Cobo, F. Stimberg, N. Casagrande, D. Grewe, S. Noury, S. Dieleman, E. Elsen, N. Kalchbrenner, H. Zen, A. Graves, H. King, T. Walters, D. Belov, and D. Hassabis, “Parallel wavenet: Fast high-fidelity speech synthesis,” 2017
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J.-M. Valin and J. Skoglund, “Lpcnet: Improving neural speech synthesis through linear prediction,” 2018
2018
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Z. Jin, A. Finkelstein, G. J. Mysore, and J. Lu, “FFTNet: a real-time speaker-dependent neural vocoder,” in
2018
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R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow-based generative network for speech synthesis,” 2018
2018
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W. Ping, K. Peng, and J. Chen, “Clarinet: Parallel wave generation in end-to-end text-to-speech,” 2018
2018
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X. Wang and J. Yamagishi, “Neural harmonic-plus-noise waveform model with trainable maximum voice frequency for text-to-speech synthesis,” 2019
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K. Kumar, R. Kumar, T. de Boissiere, L. Gestin, W. Z. Teoh, J. Sotelo, A. de Brebisson, Y. Bengio, and A. Courville, “Melgan: Generative adversarial networks for conditional waveform synthesis,” 2019
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L. Liu, H. Jiang, P. He, W. Chen, X. Liu, J. Gao, and J. Han, “On the variance of the adaptive learning rate and beyond,” 2019
2019
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X. Wang, S. Takaki, and J. Yamagishi, “Neural source-filter waveform models for statistical parametric speech synthesis,” 2019
2019
Cited alongside, same era.
K. M. Christophe Veaux, Junichi Yamagishi,
Cited in the paper.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in
2019
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J. Engel, L. Hantrakul, C. Gu, and A. Roberts, “Ddsp: Differentiable digital signal processing,” 2020
2020
Closest in time.