dc.contributor.authorWu, Zhizheng
dc.contributor.authorKinnunen, Tomi
dc.contributor.authorChng, Eng Siong
dc.contributor.authorLi, Haizhou
dc.date.accessioned2013-10-10T08:29:45Z
dc.date.available2013-10-10T08:29:45Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationWu, 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.
dc.identifier.issn1070-9908en_US
dc.identifier.urihttp://hdl.handle.net/10220/16436
dc.description.abstractA 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.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE signal processing lettersen_US
dc.rights© 2012 IEEEen_US
dc.subjectDRNTU::Engineering::Computer science and engineering
dc.titleMixture of factor analyzers using priors from non-parallel speech for voice conversionen_US
dc.typeJournal Article
dc.contributor.researchTemasek Laboratoriesen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/LSP.2012.2225615


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