mlsanityData to 7 Oct 2026

Universal Neural Vocoding with Parallel WaveNet

arXiv 2102.01106 ↗First posted 1 Feb 2021eess.AS cs.CL cs.SDcited by 25

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Abstract (arXiv)

We present a universal neural vocoder based on Parallel WaveNet, with an additional conditioning network called Audio Encoder. Our universal vocoder offers real-time high-quality speech synthesis on a wide range of use cases. We tested it on 43 internal speakers of diverse age and gender, speaking 20 languages in 17 unique styles, of which 7 voices and 5 styles were not exposed during training. We show that the proposed universal vocoder significantly outperforms speaker-dependent vocoders overall. We also show that the proposed vocoder outperforms several existing neural vocoder architectures in terms of naturalness and universality. These findings are consistent when we further test on more than 300 open-source voices.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 17 of its 27 references that are in the map. First posted on arXiv 1 Feb 2021.

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17 of 27 references.

References · 17

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  4. 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 800
  5. 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 639
  6. 2019MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesiscited by 351
  7. 2019Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogramcited by 337
  8. 2018WaveGlow: A Flow-based Generative Network for Speech Synthesiscited by 308
  9. 2018Efficient Neural Audio Synthesiscited by 218
  10. 2017Parallel WaveNet: Fast High-Fidelity Speech Synthesiscited by 214
  11. 2017Deep Voice: Real-time Neural Text-to-Speechcited by 83
  12. 2020End-to-End Adversarial Text-to-Speechcited by 77
5 more
  1. 2017Deep Voice 2: Multi-Speaker Neural Text-to-Speechcited by 75
  2. 2018ClariNet: Parallel Wave Generation in End-to-End Text-to-Speechcited by 70
  3. 2018Fast Spectrogram Inversion using Multi-head Convolutional Neural Networkscited by 21
  4. 2018A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesiscited by 3
  5. 2020Parallel WaveNet conditioned on VAE latent vectorscited by 3

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Citing papers per year: 2021: 4; 2022: 10; 2023: 7; 2024: 3; 2025: 1.

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  1. 2022BigVGAN: A Universal Neural Vocoder with Large-Scale Trainingcited by 245
  2. 2021A Survey on Neural Speech Synthesiscited by 182
  3. 2024BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of datacited by 74
  4. 2024Towards Controllable Speech Synthesis in the Era of Large Language Models: A Systematic Surveycited by 22
  5. 2024MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Modelscited by 19
  6. 2021Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speechcited by 14
  7. 2022Cross-speaker style transfer for text-to-speech using data augmentationcited by 9
  8. 2022Distribution augmentation for low-resource expressive text-to-speechcited by 5
  9. 2022Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosodycited by 5
  10. 2022Unify and Conquer: How Phonetic Feature Representation Affects Polyglot Text-To-Speech (TTS)cited by 5
  11. 2022Computer-assisted Pronunciation Training -- Speech synthesis is almost all you needcited by 4
  12. 2022Mix and Match: An Empirical Study on Training Corpus Composition for Polyglot Text-To-Speech (TTS)cited by 4
13 more
  1. 2023Controllable Emphasis with zero data for text-to-speechcited by 4
  2. 2021Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speechcited by 3
  3. 2023Modelling low-resource accents without accent-specific TTS frontendcited by 3
  4. 2022Automatic Evaluation of Speaker Similaritycited by 2
  5. 2022GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversioncited by 2
  6. 2022Controllable Accented Text-to-Speech Synthesiscited by 2
  7. 2023Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speechcited by 2
  8. 2023AE-Flow: AutoEncoder Normalizing Flowcited by 2
  9. 2025Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancementscited by 2
  10. 2023Creating New Voices using Normalizing Flowscited by 1
  11. 2021Improving the expressiveness of neural vocoding with non-affine Normalizing Flowscited by 0
  12. 2023Cross-lingual Knowledge Distillation via Flow-based Voice Conversion for Robust Polyglot Text-To-Speechcited by 0
  13. 2023Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer Learningcited by 0