Improving Recognition-Synthesis Based any-to-one Voice Conversion with Cyclic Training
Author:
Affiliation:
1. University of Science and Technology of China,National Engineering Laboratory for Speech and Language Information Processing,Hefei,P.R. China
2. iFLYTEK Co., Ltd.,iFLYTEK Research,Hefei,P.R. China
Funder
Nature
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747140.pdf?arnumber=9747140
Reference30 articles.
1. Restructuring speech representations using a pitch-adaptive time–frequency smoothing and an instantaneous-frequency-based F0 extraction: Possible role of a repetitive structure in sounds
2. Voice conversion using speaker-dependent conditional restricted Boltzmann machine
3. Voice conversion using deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks
4. Sequence-to-Sequence Acoustic Modeling for Voice Conversion
5. Phonetic posteriorgrams for many-to-one voice conversion without parallel data training;sun;ICME,2016
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