“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 of audio deepfakes is less than 16 kHz, perceived speech quality drops significantly, and a higher sampling rate may give way to higher audio quality.
“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
The Introduction states that perceived speech quality drops significantly below a 16 kHz sampling rate and that higher rates produce higher-quality speech. It makes this point about speech synthesis generally, not specifically about audio deepfakes; the paper does not establish the claimed effect for that category. (LLM, conf=0.84)
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Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus
Huang, R., Chen, F., Ren, Y., Liu, J., Cui, C., & Zhao, Z. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475437 · Cited by 76 · see citing works
ConvS2S-VC: Fully Convolutional Sequence-to-Sequence Voice Conversion
Kameoka, H., Tanaka, K., Kwaśny, D., Kaneko, T., & Hojo, N. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3001456 · Cited by 75 · see citing works
Maskcyclegan-VC: Learning Non-Parallel Voice Conversion with Filling in Frames
Kaneko, T., Kameoka, H., Tanaka, K., & Hojo, N. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414851 · Cited by 75 · see citing works
Opencpop: A High-Quality Open Source Chinese Popular Song Corpus for Singing Voice Synthesis
Wang, Y., Wang, X., Zhu, P., Wu, J., Li, H., Xue, H., Zhang, Y., Xie, L., & Bi, M. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-48 · Cited by 74 · see citing works
FastPitch: Parallel Text-to-speech with Pitch PredictionPreprint
Łańcucki, A. (2020) · arXiv (Cornell University) · doi:10.17023/03nj-k027 · Cited by 74 · see citing works
Generative Speech Coding with Predictive Variance Regularization
Kleijn, W. B., Storus, A., Chinen, M., Denton, T., Lim, F. S. C., Luebs, A., Skoglund, J., & Yeh, H. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9415120 · Cited by 74 · see citing works
Hi-Fi Multi-Speaker English TTS Dataset
Bakhturina, E., Lavrukhin, V., Ginsburg, B., & Zhang, Y. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1599 · Cited by 82 · see citing works
Kaneko, T., Tanaka, K., Kameoka, H., & Seki, S. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746713 · Cited by 72 · see citing works
Ge, L., Liao, W., Wang, S., Bak‐Jensen, B., & Pillai, J. R. (2020) · IEEE Access · doi:10.1109/access.2020.2989350 · Cited by 72 · see citing works
Shen, J., Ye, J., Chrzanowski, M., Zhang, Y., Elias, I., Zen, H., & Wu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04301 · Cited by 71 · see citing works
NAUTILUS: A Versatile Voice Cloning System
Luong, H.-T., & Yamagishi, J. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3034994 · Cited by 69 · see citing works
StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization
Mustafa, A. K., Pia, N., & Fuchs, G. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413605 · Cited by 68 · see citing works
AI-Generated Content (AIGC) for Various Data Modalities: A Survey
Foo, L. G., Rahmani, H., & Liu, J. (2025) · ACM Computing Surveys · doi:10.1145/3728633 · Cited by 98 · see citing works
Generative-Based Fusion Mechanism for Multi-Modal Tracking
Tang, Z., Xu, T., Wu, X., Zhu, X., & Kittler, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i6.28325 · Cited by 68 · see citing works
Aligntts: Efficient Feed-Forward Text-to-Speech System Without Explicit Alignment
Zeng, Z., Wang, J., Cheng, N., Xia, T., & Xiao, J. Z. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054119 · Cited by 65 · see citing works
VocGAN: A High-Fidelity Real-Time Vocoder with a Hierarchically-Nested Adversarial Network
Yang, J., Lee, J., Kim, Y.-I., Cho, H. Y., & Kim, I. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1238 · Cited by 64 · see citing works
TrojDiff: Trojan Attacks on Diffusion Models with Diverse Targets
Chen, W., Song, D. X., & Li, B. H. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00393 · Cited by 67 · see citing works
Discrete Flows: Invertible Generative Models of Discrete DataPreprint
Tran, D., Vafa, K., Agrawal, K. K., Dinh, L., & Poole, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10347 · Cited by 70 · see citing works
Upsampling Artifacts in Neural Audio Synthesis
Pons, J., Pascual, S., Cengarle, G., & Serrà, J. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414913 · Cited by 63 · see citing works
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling
Lee, J., & Han, S. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-36 · Cited by 66 · see citing works
Gu, Y., Yin, X., Rao, Y., Wan, Y., Tang, B., Zhang, Y., Chen, J., Wang, Y., & Ma, Z. (2021) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp49672.2021.9362104 · Cited by 62 · see citing works
PU-Flow: A Point Cloud Upsampling Network With Normalizing Flows
Mao, A., Du, Z., Hou, J., Duan, Y., Liu, Y., & He, Y. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3196334 · Cited by 61 · see citing works
SoftFlow: Probabilistic Framework for Normalizing Flow on ManifoldsPreprint
Kim, H., Lee, H. S., Kang, W. H., Lee, J. Y., & Kim, N. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04604 · Cited by 59 · see citing works
Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs
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RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
Gao, S., Li, Z., Qi, H., Cheng, M., & Wang, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183829 · Cited by 57 · see citing works
A Study of Non-autoregressive Model for Sequence Generation
Ren, Y., Liu, J., Tan, X., Zhao, Z., Zhao, S., & Liu, T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.15 · Cited by 56 · see citing works
RAVE: A variational autoencoder for fast and high-quality neural audio synthesisPreprint
Caillon, A., & Esling, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05011 · Cited by 56 · see citing works
Domain Generalization via Aggregation and Separation for Audio Deepfake Detection
Xie, Y., Cheng, H., Wang, Y., & Ye, L. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3324724 · Cited by 59 · see citing works
Mylonas, C., Abdallah, I., & Chatzi, E. (2021) · Wind Energy · doi:10.1002/we.2621 · Cited by 53 · see citing works
PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows
Mao, A., Du, Z., Wen, Y., Xuan, J., & Liu, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20062-5_23 · Cited by 53 · see citing works
Generative adversarial networks and their application to 3D face generation: A survey
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Dataset of Speech Production in intracranial Electroencephalography
Verwoert, M., Ottenhoff, M. C., Goulis, S., Colon, A., Wagner, L., Tousseyn, S., van Dijk, J. P., Kubben, P., & Herff, C. (2022) · Scientific Data · doi:10.1038/s41597-022-01542-9 · Cited by 51 · see citing works
Pre-Trained Text Embeddings for Enhanced Text-to-Speech Synthesis
Hayashi, T., Watanabe, S., Toda, T., Takeda, K., Toshniwal, S., & Livescu, K. (2019) · Interspeech · doi:10.21437/interspeech.2019-3177 · Cited by 76 · see citing works
Sequence-to-Sequence Singing Synthesis Using the Feed-Forward Transformer
Blaauw, M., & Bonada, J. (2019) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp40776.2020.9053944 · Cited by 57 · see citing works
- 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 · 2018: 3 · 2012: 1
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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