Transfer Learning for Low-Resource, Multi-Lingual, and Zero-Shot Multi-Speaker Text-to-Speech
Author:
Affiliation:
1. Kakao Enterprise, Seongnam, Republic of Korea
2. Institute of New Media and Communications, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
Funder
Institute of Information and communications Technology Planning and Evaluation
Korea government
Development of Ultra-high Speech Quality Technology for Remote Multi-speaker Conference System
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6570655/10304349/10428082.pdf?arnumber=10428082
Reference53 articles.
1. Diff-TTS: A Denoising Diffusion Model for Text-to-Speech
2. Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
3. Fastspeech 2: Fast and high-quality end-to-end text to speech;Ren,2021
4. Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech;Kim,2021
5. Glow-TTS: A generative flow for text-to-speech via monotonic alignment search;Kim,2020
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