Neural HMMS Are All You Need (For High-Quality Attention-Free TTS)
Author:
Affiliation:
1. KTH Royal Institute of Technology,Division of Speech, Music and Hearing,Stockholm,Sweden
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09746686.pdf?arnumber=9746686
Reference38 articles.
1. Normalizing flow based hidden Markov models for classification of speech phones with explainability;ghosh,2021
2. Speaking Speed Control of End-to-End Speech Synthesis Using Sentence-Level Conditioning
3. Non-parametric duration modelling for speech synthesis with a joint model of acoustics and duration;henter;IEICE Tech Rep,2017
4. Robust TTS duration modelling using DNNS
5. Measuring the perceptual effects of modelling assumptions in speech synthesis using stimuli constructed from repeated natural speech
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