The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
“Waveglow: a flow-based generative network for speech synthesis,”
Open accessAbstract
In this paper we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from Glow [1] and WaveNet [2] in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. Our PyTorch implementation produces audio samples at a rate of more than 500 kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio quality as good as the best publicly available WaveNet implementation. All code will be made publicly available online [3].
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“When the sampling frequency (sampling rate) of the audio deepfakes is less than 16 kHz, perceived speech quality of audio deepfakes drops significantly, and the higher sampling rate may give way to higher audio quality (Prenger et al., 2019).”
From Audio deepfakes: A survey (2023) · cites this paper as Prenger et al., 2019
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Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerrv-Ryan, R., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461368 · Cited by 2,791
Variational Inference with Normalizing FlowsPreprint
Rezende, D. J., & Mohamed, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05770 · Cited by 2,583
Signal estimation from modified short-time Fourier transform
Griffin, D. W., & Lim, J. S. (1984) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1984.1164317 · Cited by 2,085
Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2016) · arXiv · Cited by 2,067
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural NetworksPreprint
Salimans, T., & Kingma, D. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.07868 · Cited by 1,551
NICE: Non-linear Independent Components Estimation
Dinh, L., Krueger, D., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1410.8516 · Cited by 1,493
Improving Variational Inference with Inverse Autoregressive FlowPreprint
Kingma, D., Salimans, T., Józefowicz, R., Chen, X., Sutskever, I., & Welling, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04934 · Cited by 1,168
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, Ł., Shazeer, N., Ku, A., & Tran, D. (2018) · arXiv · doi:10.48550/arxiv.1802.05751 · Cited by 1,085
Research works printed but not linked: 2
- ito, 2017, The LJ speech dataset
- yamamoto, 2018, Wavenet vocoder
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Items 1,001–1,002 of 1,002
MaoSpeechNet: A Pre-Trained Framework for Text-to-Speech Synthesis in Mao Language
Adahrii, P., Majhi, M. K., & Saha, S. K. (2026) · International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT) · doi:10.1109/ic-sit69723.2026.11735742
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
Ren, Y., Hu, C., Tan, X., Qin, T., Zhao, S., Zhao, Z., & Liu, T.-Y. (2020) · arXiv · doi:10.48550/arxiv.2006.04558 · Cited by 580
Metadata
- Authors
- Prenger, R., Valle, R., & Catanzaro, B.
- Affiliations
- Nvidia (United States) (US)
- ORCID
- Catanzaro, Bryan
- Venue
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 3617-3621
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2898847420
- Cited by
- 1,004
- References
- 22
- Citations by year
- 2026: 39 · 2025: 86 · 2024: 111 · 2023: 156 · 2022: 161 · 2021: 220 · 2020: 169 · 2019: 58
- FWCI
- 58.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- WaveGlow, speech synthesis, flow-based generative models, glow, WaveNet, audio generation, likelihood maximization, high-quality audio
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