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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Cites
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van den Oord, A., Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., van den Driessche, G., Lockhart, E., Cobo, L. C., Stimberg, F., et al. (2017) · arXiv (Cornell University) · Cited by 262
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Cited by
Sorted by citations
Denoising Diffusion Probabilistic Models
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Normalizing Flows: An Introduction and Review of Current Methods
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HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech SynthesisPreprint
Kong, J., Kim, J., & Bae, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.05646 · Cited by 1,269
Normalizing Flows for Probabilistic Modeling and InferencePreprint
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., & Lakshminarayanan, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.02762 · Cited by 1,280
FastSpeech: Fast, Robust and Controllable Text to SpeechPreprint
Ren, Y., Ruan, Y., Tan, X., Qin, T., Zhao, S., Zhao, Z., & Liu, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.09263 · Cited by 724
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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