Prosody Modelling With Pre-Trained Cross-Utterance Representations for Improved Speech Synthesis
Author:
Affiliation:
1. JD AI research, Beijing, China
2. Department of Electrical Engineering, Tsinghua University, Beijing, China
Funder
National Key R&D Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics
Link
http://xplorestaging.ieee.org/ielx7/6570655/9970249/10129796.pdf?arnumber=10129796
Reference56 articles.
1. Hierarchical generative modeling for controllable speech synthesis;hsu;Proc Int Conf Learn Representations,0
2. Towards end-to-end prosody transfer for expressive speech synthesis with tacotron;skerry-ryan;Proc 35th Int Conf Mach Learn,0
3. Layer-Wise Analysis of a Self-Supervised Speech Representation Model
4. Comparing acoustic and textual representations of previous linguistic context for improving Text-to-Speech
5. A review of reading prosody acquisition and development
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. HIGNN-TTS: Hierarchical Prosody Modeling With Graph Neural Networks for Expressive Long-Form TTS;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16
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