mlsanityData to 7 Oct 2026

Hierarchical Prosody Modeling for Non-Autoregressive Speech Synthesis

arXiv 2011.06465 ↗First posted 12 Nov 2020eess.AS cs.LG cs.SDcited by 16

Not on the map. Why?

Abstract (arXiv)

Prosody modeling is an essential component in modern text-to-speech (TTS) frameworks. By explicitly providing prosody features to the TTS model, the style of synthesized utterances can thus be controlled. However, predicting natural and reasonable prosody at inference time is challenging. In this work, we analyzed the behavior of non-autoregressive TTS models under different prosody-modeling settings and proposed a hierarchical architecture, in which the prediction of phoneme-level prosody features are conditioned on the word-level prosody features. The proposed method outperforms other competitors in terms of audio quality and prosody naturalness in our objective and subjective evaluation.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Nov 2020.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 6; 2022: 5; 2023: 3; 2024: 0; 2025: 2.

16 papers.

Sort:

By area:

  1. 2021A Survey on Neural Speech Synthesiscited by 182
  2. 2022A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyondcited by 64
  3. 2022ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speechcited by 22
  4. 2021Hierarchical prosody modeling and control in non-autoregressive parallel neural TTScited by 9
  5. 2023CorrTalk: Correlation Between Hierarchical Speech and Facial Activity Variances for 3D Animationcited by 8
  6. 2022Unsupervised word-level prosody tagging for controllable speech synthesiscited by 7
  7. 2023ADAPTERMIX: Exploring the Efficacy of Mixture of Adapters for Low-Resource TTS Adaptationcited by 7
  8. 2025Voice Cloning: Comprehensive Surveycited by 4
  9. 2021Improved Prosodic Clustering for Multispeaker and Speaker-independent Phoneme-level Prosody Controlcited by 3
  10. 2022Controllable speech synthesis by learning discrete phoneme-level prosodic representationscited by 3
  11. 2021Improving Prosody for Unseen Texts in Speech Synthesis by Utilizing Linguistic Information and Noisy Datacited by 2
  12. 2021UniTTS: Residual Learning of Unified Embedding Space for Speech Style Controlcited by 1
4 more
  1. 2021Multi-Scale Spectrogram Modelling for Neural Text-to-Speechcited by 0
  2. 2022Predicting phoneme-level prosody latents using AR and flow-based Prior Networks for expressive speech synthesiscited by 0
  3. 2023KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generationcited by 0
  4. 2025ProMode: A Speech Prosody Model Conditioned on Acoustic and Textual Inputscited by 0