Mixture of factor analyzers using priors from non-parallel speech for voice conversion

A robust voice conversion function relies on a large amount of parallel training data, which is difficult to collect in practice. To tackle the sparse parallel training data problem in voice conversion, this paper describes a mixture of factor analyzers method which integrates prior knowledge from n...

全面介紹

Saved in:

書目詳細資料
Main Authors:	Wu, Zhizheng, Kinnunen, Tomi, Chng, Eng Siong, Li, Haizhou
其他作者:	School of Computer Engineering
格式:	Article
語言:	English
出版:	2013
主題:	DRNTU::Engineering::Computer science and engineering
在線閱讀:	https://hdl.handle.net/10356/102726 http://hdl.handle.net/10220/16436
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Nanyang Technological University
語言:	English

id	sg-ntu-dr.10356-102726
record_format	dspace
spelling	sg-ntu-dr.10356-1027262020-05-28T07:18:12Z Mixture of factor analyzers using priors from non-parallel speech for voice conversion Wu, Zhizheng Kinnunen, Tomi Chng, Eng Siong Li, Haizhou School of Computer Engineering Temasek Laboratories DRNTU::Engineering::Computer science and engineering A robust voice conversion function relies on a large amount of parallel training data, which is difficult to collect in practice. To tackle the sparse parallel training data problem in voice conversion, this paper describes a mixture of factor analyzers method which integrates prior knowledge from non-parallel speech into the training of conversion function. The experiments on CMU ARCTIC corpus show that the proposed method improves the quality and similarity of converted speech. With both objective and subjective evaluations, we show the proposed method outperforms the baseline GMM method. 2013-10-10T08:29:45Z 2019-12-06T20:59:37Z 2013-10-10T08:29:45Z 2019-12-06T20:59:37Z 2012 2012 Journal Article Wu, Z., Kinnunen, T., Chng, E. S., & Li, H. (2012). Mixture of factor analyzers using priors from non-parallel speech for voice conversion. IEEE signal processing letters, 19(12), 914-917. 1070-9908 https://hdl.handle.net/10356/102726 http://hdl.handle.net/10220/16436 10.1109/LSP.2012.2225615 en IEEE signal processing letters © 2012 IEEE
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Computer science and engineering
spellingShingle	DRNTU::Engineering::Computer science and engineering Wu, Zhizheng Kinnunen, Tomi Chng, Eng Siong Li, Haizhou Mixture of factor analyzers using priors from non-parallel speech for voice conversion
description	A robust voice conversion function relies on a large amount of parallel training data, which is difficult to collect in practice. To tackle the sparse parallel training data problem in voice conversion, this paper describes a mixture of factor analyzers method which integrates prior knowledge from non-parallel speech into the training of conversion function. The experiments on CMU ARCTIC corpus show that the proposed method improves the quality and similarity of converted speech. With both objective and subjective evaluations, we show the proposed method outperforms the baseline GMM method.
author2	School of Computer Engineering
author_facet	School of Computer Engineering Wu, Zhizheng Kinnunen, Tomi Chng, Eng Siong Li, Haizhou
format	Article
author	Wu, Zhizheng Kinnunen, Tomi Chng, Eng Siong Li, Haizhou
author_sort	Wu, Zhizheng
title	Mixture of factor analyzers using priors from non-parallel speech for voice conversion
title_short	Mixture of factor analyzers using priors from non-parallel speech for voice conversion
title_full	Mixture of factor analyzers using priors from non-parallel speech for voice conversion
title_fullStr	Mixture of factor analyzers using priors from non-parallel speech for voice conversion
title_full_unstemmed	Mixture of factor analyzers using priors from non-parallel speech for voice conversion
title_sort	mixture of factor analyzers using priors from non-parallel speech for voice conversion
publishDate	2013
url	https://hdl.handle.net/10356/102726 http://hdl.handle.net/10220/16436
_version_	1681057254843875328

Mixture of factor analyzers using priors from non-parallel speech for voice conversion

相似書籍