Paper Info
Title
Spectrum and Prosody Conversion for Cross-lingual Voice Conversion with CycleGAN
Abstract
Cross-lingual voice conversion aims to change source speaker's voice to sound like that of target speaker, when source and target speakers speak different languages. It relies on nonparallel training data from two different languages, hence, is more challenging than mono-lingual voice conversion. Previous studies on cross-lingual voice conversion mainly focus on spectral conversion with a linear transformation for F0 transfer. However, as an important prosodic factor, F0 is inherently hierarchical, thus it is insufficient to just use a linear method for conversion. We propose the use of continuous wavelet transform (CWT) decomposition for F0 modeling. CWT provides a way to decompose a signal into different temporal scales that explain prosody in different time resolutions. We also propose to train two CycleGAN pipelines for spectrum and prosody mapping respectively. In this way, we eliminate the need for parallel data of any two languages and any alignment techniques. Experimental results show that our proposed Spectrum-Prosody-CycleGAN framework outperforms the Spectrum-CycleGAN baseline in subjective evaluation. To our best knowledge, this is the first study of prosody in cross-lingual voice conversion.
Year
Venue
Keywords
2020
2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Cross-lingual voice conversion,prosody,CycleGAN,continuous wavelet transform
DocType
ISSN
ISBN
Conference
2640-009X
978-1-7281-8130-1
Citations 
PageRank 
References 
0
0.34
36
Authors
4
Name
Order
Citations
PageRank
Zongyang Du100.34
Kun Zhou200.34
Berrak Sisman36010.34
Haizhou Li43678334.61