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].
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Cited by
Sorted by citations
Items 801–900 of 1,002
MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in FramesPreprint
Kaneko, T., Kameoka, H., Tanaka, K., & Hojo, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.12841 · Cited by 1
Hono, Y., Takaki, S., Hashimoto, K., Oura, K., Nankaku, Y., & Tokuda, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.07786 · Cited by 1
Improve GAN-based Neural Vocoder using Pointwise Relativistic LeastSquare GANPreprint
Wang, C., Chen, Y., Wang, B., & Shi, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14245 · Cited by 1
Tobing, P. L., & Toda, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.09856 · Cited by 1
Basis-MelGAN: Efficient Neural Vocoder Based on Audio DecompositionPreprint
Liu, Z., & Qian, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.13419 · Cited by 1
Automated Testing of AI ModelsPreprint
Haldar, S., Vijaykeerthy, D., & Saha, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.03320 · Cited by 1
Guided-TTS:Text-to-Speech with Untranscribed SpeechPreprint
Kim, H., Kim, S., & Yoon, S. (2021) · arXiv (Cornell University) · Cited by 1
Ahmad, A., Selim, M. R., Iqbal, M. Z., & Rahman, M. S. (2022) · Research Square · doi:10.21203/rs.3.rs-1690533/v1 · Cited by 1
Deep Neural Networks for Automatic Speech-To-Speech Translation of Open Educational Resources
Pérez-González-de-Martos, A. (2022) · doi:10.4995/thesis/10251/184019 · Cited by 1
A flexible speech feature converter based on an enhanced architecture of U-net
Yue, Y. (2020) · doi:10.32469/10355/94697 · Cited by 1
Komeiji, S., Shigemi, K., Mitsuhashi, T., Iimura, Y., Suzuki, H., Sugano, H., Shinoda, K., Yatabe, K., & Tanaka, T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.21.608927 · Cited by 1
Audiovisual speech representation learning applied to emotion recognition
Sadok, S. (2024) · theses.fr (ABES) · doi:10.70675/0f15a36fzecf8z4cffz94e7z120e7d8fd39d · Cited by 1
A Fully Time-domain Neural Model for Subband-based Speech SynthesizerPreprint
Rabiee, A., Kim, G., Kim, T.-H., & Lee, S.-Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.05319
WaveTract: A hybrid generative model for speech synthesis
Englert, B. B., Zainkó, C., & Németh, G. (2019) · International Conference on Speech Technology and Human-Computer Dialogue (SpeD) · doi:10.1109/sped.2019.8906280
Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density NetworkPreprint
Hwang, M.-J., Song, E., Yamamoto, R., Soong, F. K., & Kang, H.-G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.11686
A comparison of Vietnamese Statistical Parametric Speech Synthesis Systems
Phan, H. K., Phung, V. L., Dinh, A. T., & Nguyen, B. (2020) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse50997.2020.9287553
Articulatory-WaveNet: Autoregressive Model For Acoustic-to-Articulatory InversionPreprint
Bozorg, N., & Johnson, M. T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12594
Dimensionality Reduction for Flow-Based Face Embeddings
Poliakov, S., & Belykh, I. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-4218-3_36
Online Speaker Adaptation for WaveNet-based Neural VocodersPreprint
Huang, Q., Ai, Y., & Ling, Z.-H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.06182
Robust Noisy Speech Parameterization Using Convolutional Neural Networks
Vashkevich, R., & Azarov, E. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60276-5_58
Improving Sequential Latent Variable Models with Autoregressive FlowsPreprint
Marino, J., Chen, L., He, J., & Mandt, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.03172
On the Training and Testing Data Preparation for End-to-End Text-to-Speech Application
Tran, D. C., Khan, M. K. A. A., & Sridevi, S. H. (2020) · IEEE Control and System Graduate Research Colloquium (ICSGRC) · doi:10.1109/icsgrc49013.2020.9232605
Can We Trust Deep Speech Prior?Preprint
Shi, Y., Chen, H., Tang, Z., Li, L., Wang, D., & Han, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.02110
Denoising-and-Dereverberation Hierarchical Neural Vocoder for Robust Waveform GenerationPreprint
Ai, Y., Li, H., Wang, X., Yamagishi, J., & Ling, Z.-H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03955
Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet VocoderPreprint
Davis, S., Coccia, G., Gooch, S., & Mack, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.10469 · Cited by 2
Comparative Study on Neural Vocoders for Multispeaker Text-To-Speech Synthesis
Rajan, R., Roopan, A., Prakash, S., Jose, E., & Sati, P. (2020) · IEEE Recent Advances in Intelligent Computational Systems · doi:10.1109/raics51191.2020.9332514
Text-to-speech for the hearing impairedPreprint
Schlittenlacher, J., & Baer, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.02174
ItôTTS and ItôWave: Linear Stochastic Differential Equation Is All You Need For Audio GenerationPreprint
Wu, S., & Shi, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.07583 · Cited by 8
即時中文語音合成系統(Real-Time Mandarin Speech Synthesis System)
Cheng, A., & Chen, C.-P. (2019) · International Conference on Computational Linguistics
Real-Time Single-Speaker Taiwanese-Accented Mandarin Speech Synthesis System
Wang, Y.-W., & Chen, C.-P. (2020) · International Conference on Computational Linguistics
Improving the Expressiveness of Neural Vocoding with Non-Affine Normalizing Flows
Gabryś, A., Jiao, Y., Klimkov, V., Korzekwa, D., & Barra-Chicote, R. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1555
EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech ModelPreprint
Cui, C., Ren, Y., Liu, J., Chen, F., Huang, R., Lei, M., & Zhao, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09317
Mathematical Vocoder Algorithm : Modified Spectral Inversion for Efficient Neural Speech SynthesisPreprint
Ryu, H. G., Kim, J., & See, S. C. W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.03167
Relational Data Selection for Data Augmentation of Speaker-dependent Multi-band MelGAN VocoderPreprint
Wu, Y.-C., Hu, C.-H., Lee, H.-S., Peng, Y., Huang, W.-C., Tsao, Y., Wang, H., & Toda, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05629
Sprachsynthese -- State-of-the-Art in englischer und deutscher SprachePreprint
Peinl, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06230
PU-Flow: a Point Cloud Upsampling Networkwith Normalizing Flows.Preprint
Mao, A., Du, Z., Hou, J., Duan, Y., Liu, Y., & He, Y. (2021) · arXiv (Cornell University)
An Empirical Study on End-to-End Singing Voice Synthesis with Encoder-Decoder ArchitecturesPreprint
Ke, D., Lu, Y., Liu, X., Xu, Y., Sun, J., & Cai, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.03008
AnyoneNet: Synchronized Speech and Talking Head Generation for Arbitrary PersonPreprint
Wang, X., Xie, Q., Zhu, J., Xie, L., & Scharenborg, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.04325
Voice Reconstruction from Silent Speech with a Sequence-to-Sequence ModelPreprint
Li, H., Lin, H., Wang, Y., Wang, H., Gao, H., Ai, Q., & Li, G. (2021) · arXiv (Cornell University)
Research on the Methods of Speech Synthesis Technology
Hu, J., & Hamdulla, A. (2021) · IEEE International Conference on Pattern Recognition and Machine Learning (PRML) · doi:10.1109/prml52754.2021.9520718
Towards Universal Neural Vocoding with a Multi-band Excited WaveNetPreprint
Röebel, A., & Bous, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.03329
Conditional Generative Adversarial Network with One-Dimensional Self-attention for Speech Synthesis
Javeri, Y., Jain, N., & Bagul, S. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-16-2248-9_23
GANtron: Emotional Speech Synthesis with Generative Adversarial NetworksPreprint
Hortal, E., & Alarcia, R. B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.03390
Kang, J. (2021) · CUNY Academic Works (City University of New York)
Sequence-To-Sequence Voice Conversion using F0 and Time Conditioning and Adversarial LearningPreprint
Bous, F., Benaroya, L., Obin, N., & Röebel, A. (2021) · arXiv (Cornell University)
Text-to-speech with linear spectrogram prediction for quality and speed improvement
Yoon, H., & Nam, H. (2021) · Phonetics and Speech Sciences · doi:10.13064/ksss.2021.13.3.071
DeepA: A Deep Neural Analyzer For Speech And Singing VocodingPreprint
Nikonorov, S., Şişman, B., Zhang, M., & Li, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06434
Activation Maximization with a Prior in Speech Data
Inoue, S., & Gonsalves, T. (2021) · American Journal of Computer Science and Technology · doi:10.11648/j.ajcst.20210403.13
Development of Digital Storytelling Platform for Children based on Emotions
Vijayakumaran, T., Thavachelvam, T., Gnaeswaran, A., Sandanayake, T., Sumathipala, K., & Bandara, S. (2021) · International Conference on Information Technology Research (ICITR) · doi:10.1109/icitr54349.2021.9657431 · Cited by 1
A Fast and Lightweight Text-To-Speech Model with Spectrum and Waveform Alignment Algorithms
Jeong, K., Nguyen, H.-K., & Kang, H.-G. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9616247
End-to-end Indonesian Speech Synthesis Based On Transfer Learning And Alternate Training
Lu, Y., Yang, J., & Yang, R. (2021) · International Conference on Asian Language Processing · doi:10.1109/ialp54817.2021.9675251
Towards Using Heterogeneous Relation Graphs for End-to-End TTS
Setlur, A., Madaan, A., Parekh, T., Yang, Y., & Black, A. W. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9687876
Building an Integrated Framework of Korean Text Summarization and Text-to-Speech
Kim, T., Kim, J., Kang, H., Kim, S., & Kang, P. (2022) · Journal of Korean Institute of Industrial Engineers · doi:10.7232/jkiie.2022.48.1.080
Speech Production in Intracranial Electroencephalography: iBIDS DatasetPreprint
Verwoert, M., Ottenhoff, M. C., Goulis, S., Colon, A., Wagner, L., Tousseyn, S., van Dijk, J. P., Kubben, P., & Herff, C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.05.487183
Submission from SCUT for Blizzard Challenge 2020
Zhong, J., Yang, Y., & Bu, S. (2020) · doi:10.21437/vcc_bc.2020-6
The SHNU System for Blizzard Challenge 2020
He, L., Shi, Q., Wu, L., Sun, J., He, R., Long, Y., & Liang, J. (2020) · doi:10.21437/vcc_bc.2020-2
The UFRJ Entry for the Voice Conversion Challenge 2020
da Costa, V. P., Maia, R., Quintanilha, I. M., Netto, S. L., & Biscainho, L. W. P. (2020) · doi:10.21437/vcc_bc.2020-29
Decision letter for "Grazer behaviour can regulate large‐scale patterning of community states"
(2021) · Publons reviews and discussion · doi:10.1111/ele.13828/v3/decision1
Jasir, M. P., Balakrishnan, K., & Jaseena, K. U. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0707-4_33
SinGlow: Singing Voice Synthesis with Glow: Help Virtual Singers More Human-like
Yang, H. (2021) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice54393.2021.00030
Lightweight convolution-based Chinese Speech Synthesis Method
Yang, R., Shen, Y., Yi, N., Fan, Z., Liu, J., & Wumaier, A. (2022) · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · doi:10.1109/cvidliccea56201.2022.9824329
Ahmad, A., Selim, M. R., Iqbal, M. Z., & Rahman, M. S. (2022) · Research Square · doi:10.21203/rs.3.rs-1690533/v2
Schrowave: Realistic Voice Generation by Solving Two-Stage Conditional Schrodinger Bridge ProblemsPreprint
Wu, S., & Shi, Z. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4172002
Síntese de fala aplicada à geração de conjunto de dados para reconhecimento automático de fala
Casanova, E. (2022) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.55.2022.tde-02092022-142539
Hsiao, C.-J., Chen, F., Han, J.-Y., Zheng, W.-Z., & Lai, Y.-H. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871247
Analysis of expressivity transfer in non-autoregressive end-to-end multispeaker TTS systems
Kulkarni, A., Colotte, V., & Jouvet, D. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10388
Consistency Regularization for GAN-based Neural Vocoders
Onishi, K., & Nakashika, T. (2022) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipaasc55919.2022.9980309
Bidirectional Decoding Tacotron for Attention Based Neural Speech Synthesis
Zhao, W., & Xu, L. (2022) · International Conference on Information Science and Systems (ICISS) · doi:10.1145/3561877.3561887
TTS - VLSP 2021: Development of Smartcall Vietnamese Text-to-Speech
Bao, N. Q., Hoai, L. B., Hoc, N. V., Quyen, D. B., & Phương, N. T. (2022) · VNU Journal of Science Computer Science and Communication Engineering · doi:10.25073/2588-1086/vnucsce.348
Training Scheme for Stereo Audio Generation
Mohanty, P. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-21750-0_21
Analyzing Emotional Speech and Text: A Special Focus on Bengali Language
Majumder, K., & Das, D. (2022) · Signals and communication technology · doi:10.1007/978-3-031-18444-4_15
Multi-Speaker Indonesian Speech Synthesis Based on Global Style Embedding
杨, 益. (2023) · Computer Science and Application · doi:10.12677/csa.2023.131013
Unsupervised 3D Shape Representation Learning Using Normalizing Flow
Xiang, L., Wen, C., & Huang, H. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26319-4_10
Mandarin Text-to-Speech Front-End with Lightweight Distilled Convolution Network
Zhao, W., Wang, Z., & Li, X. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3256319
Lin, Y.-T. R., & Chiang, C.-Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096801
Saravanan, R., & Aishwarya, R. (2023) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs57279.2023.10112873
A Privacy-Preserving Trajectory Mining Model
Wang, Z., Wu, S. X., Zhu, J., & Zhu, Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095384
Studying the Use of Phonetic Language Script in a Limited Training Environment for Text-to-Speech
Popli, A., Sharma, S., Basu, S., & Dalela, P. K. (2023) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin57001.2023.10116694
Jasir, M. P., Kannan, B., & Jaseena, K. U. (2023) · AIP conference proceedings · doi:10.1063/5.0138717
Tan, X. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-99-0827-1_10
Tan, X. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-99-0827-1_6 · Cited by 1
Expressive and Controllable TTS
Tan, X. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-99-0827-1_8
Tan, X. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-99-0827-1_7
Tan, X. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-99-0827-1_4
Tan, X. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-99-0827-1_1
Park, S. H., Park, H. J., Kim, Y. J., Park, Y. W., Lee, J. M., Jung, Y.-R., Jo, S.-H., Oh, S. J., & Doo, I. C. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1252-0_37
SchröWave: Realistic voice generation by solving two-stage conditional Schrödinger bridge problems
Shi, Z., & Wu, S. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104175
Improving Generative Adversarial Network-based Vocoding through Multi-scale Convolution
Li, W., Chen, Y.-T., & Tang, B. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3610532
Voice Cloning for Voice Disorders: Impact of Phonetic Content
Wadoux, L., Barbot, N., Chevelu, J., & Lolive, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40498-6_26
Regna, C., Sbattella, L., Scotti, V., Sukhov, A., & Tedesco, R. (2023) · CRC Press eBooks · doi:10.1201/9781003296126-7
Spectral and latent representation distortion for TTS evaluation
Kongthaworn, T. (2021) · doi:10.58837/chula.the.2021.101
Wang, J. L., Hyatt, J. S., & Fish, M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4576765
Adapted Methods for GAN Vocoders via Skip-Connections ISTFT and Cooperative Structure
Xiao, S., Yang, J., & Wang, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44192-9_18
A Survey on Talking Head Generation: The Methods, Status and ChallengesPreprint
Cai, Y., Qiao, P., & Li, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4576782
Hindi text to speech using transliteration and transfer learning
Jain, M., Kothari, H., Zaveri, H., Dalia, A., & Kanani, P. (2023) · AIP conference proceedings · doi:10.1063/5.0165718
NFAQP: Normalizing Flow Based Approximate Query Processing
Cen, L., Li, J., Yue, W., & Wang, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46677-9_4
Analysis of Embedding Space of Speech Synthesis System as High-Dimension, Low-Sample-Size Data
Tachioka, Y. (2023) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce59613.2023.10315574
E-TTS: Expressive Text-to-Speech Synthesis for Hindi Using Data Augmentation
Gupta, I., & Murthy, H. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48312-7_20
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
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
