Discourse-Level Prosody Modeling with a Variational Autoencoder for Non-Autoregressive Expressive Speech Synthesis
Author:
Affiliation:
1. University of Science and Technology of China,National Engineering Laboratory for Speech and Language Information Processing,Hefei,P.R.China
Funder
Nature
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09746238.pdf?arnumber=9746238
Reference25 articles.
1. Learning Latent Representations for Style Control and Transfer in End-to-end Speech Synthesis
2. Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder
3. Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speech
4. Using previous acoustic context to improve text-to-speech synthesis;oplustil-gallegos,2020
5. Dynamic Prosody Generation for Speech Synthesis Using Linguistics-Driven Acoustic Embedding Selection
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1. Stylespeech: Self-Supervised Style Enhancing with VQ-VAE-Based Pre-Training for Expressive Audiobook Speech Synthesis;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
2. Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources;EURASIP Journal on Audio, Speech, and Music Processing;2024-02-12
3. Improving Speech Prosody of Audiobook Text-To-Speech Synthesis with Acoustic and Textual Contexts;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04
4. Diffusion-Based Approach to Style Modeling in Expressive TTS;Intelligent Systems;2022
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