Mixture of Factor Analyzers Using Priors from Non-Parallel Speech for Voice Conversion
Abstract—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 knowled...
Main Authors: | , , , , |
---|---|
Other Authors: | |
Format: | Text |
Language: | English |
Subjects: | |
Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.6118 http://cs.joensuu.fi/pages/tkinnu/webpage/pdf/TMFA_IEEESPL_2012.pdf |
id |
ftciteseerx:oai:CiteSeerX.psu:10.1.1.304.6118 |
---|---|
record_format |
openpolar |
spelling |
ftciteseerx:oai:CiteSeerX.psu:10.1.1.304.6118 2023-05-15T14:59:39+02:00 Mixture of Factor Analyzers Using Priors from Non-Parallel Speech for Voice Conversion Zhizheng Wu Tomi Kinnunen Eng Siong Chng Senior Member Haizhou Li The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.6118 http://cs.joensuu.fi/pages/tkinnu/webpage/pdf/TMFA_IEEESPL_2012.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.6118 http://cs.joensuu.fi/pages/tkinnu/webpage/pdf/TMFA_IEEESPL_2012.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://cs.joensuu.fi/pages/tkinnu/webpage/pdf/TMFA_IEEESPL_2012.pdf text ftciteseerx 2016-01-07T22:13:41Z Abstract—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 nonparallel 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. Index Terms—Voice conversion, prior knowledge, factor analysis, mixture of factor analyzers. I. Text Arctic Unknown Arctic |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
ftciteseerx |
language |
English |
description |
Abstract—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 nonparallel 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. Index Terms—Voice conversion, prior knowledge, factor analysis, mixture of factor analyzers. I. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Zhizheng Wu Tomi Kinnunen Eng Siong Chng Senior Member Haizhou Li |
spellingShingle |
Zhizheng Wu Tomi Kinnunen Eng Siong Chng Senior Member Haizhou Li Mixture of Factor Analyzers Using Priors from Non-Parallel Speech for Voice Conversion |
author_facet |
Zhizheng Wu Tomi Kinnunen Eng Siong Chng Senior Member Haizhou Li |
author_sort |
Zhizheng Wu |
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 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.6118 http://cs.joensuu.fi/pages/tkinnu/webpage/pdf/TMFA_IEEESPL_2012.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
http://cs.joensuu.fi/pages/tkinnu/webpage/pdf/TMFA_IEEESPL_2012.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.6118 http://cs.joensuu.fi/pages/tkinnu/webpage/pdf/TMFA_IEEESPL_2012.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
_version_ |
1766331752435941376 |