Perturbation AUTOVC: Voice Conversion From Perturbation and Autoencoder Loss
Author:
Affiliation:
1. Department of Computer Engineering, Chosun University, Gwangju, South Korea
2. Intelligent Image Processing Research Center, Korea Electronics Technology Institute (KETI), Seongnam, South Korea
Funder
Institute of Information & Communications Technology Planning & Evaluation
Korea Government (MSIT)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10353951.pdf?arnumber=10353951
Reference69 articles.
1. An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning
2. An overview of voice conversion systems
3. Voice privacy using CycleGAN and time-scale modification
4. Non-linear frequency scale mapping for voice conversion in text-to-speech system with cepstral description
5. Cycle consistent network for end-to-end style transfer TTS training
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