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Depositordc.contributorRuus, Laine
Funderdc.contributor.otherEPSRC - Engineering and Physical Sciences Research Councilen_UK
Spatial Coveragedc.coverage.spatialUKen
Spatial Coveragedc.coverage.spatialUNITED KINGDOMen
Time Perioddc.coverage.temporalstart=2016; end=2016; scheme=W3C-DTFen
Data Creatordc.creatorToda, Tomoki
Data Creatordc.creatorChen, Ling-Hui
Data Creatordc.creatorSaito, Daisuke
Data Creatordc.creatorVillavicencio, Fernando
Data Creatordc.creatorWester, Mirjam
Data Creatordc.creatorWu, Zhizheng
Data Creatordc.creatorYamagishi, Junichi
Citationdc.identifier.citationToda, Tomoki; Chen, Ling-Hui; Saito, Daisuke; Villavicencio, Fernando; Wester, Mirjam; Wu, Zhizheng; Yamagishi, Junichi. (2016). The Voice Conversion Challenge, 2016: multidimensional scaling (MDS) listening test results, 2016 [dataset]. University of Edinburgh. School of Informatics. Centre for Speech Technology Research.
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Dataset Description (abstract)dc.description.abstractThe Voice Conversion Challenge (VCC) 2016, one of the special sessions at Interspeech 2016, deals with speaker identity conversion, referred as Voice Conversion (VC). The task of the challenge was speaker conversion, i.e., to transform the voice identity of a source speaker into that of a target speaker while preserving the linguistic content. Using a common dataset consisting of 162 utterances for training and 54 utterances for evaluation from each of 5 source and 5 target speakers, 17 groups working in VC around the world developed their own VC systems for every combination of the source and target speakers, i.e., 25 systems in total, and generated voice samples converted by the developed systems. The objective of the VCC was to compare various VC techniques on identical training and evaluation speech data. The samples were evaluated in terms of target speaker similarity and naturalness by 200 listeners in a controlled environment. This section of the VCC repository contains the listening test results for four of the source-target pairs (two intra-gender and two cross-gender) in more detail. Multidimensional scaling was performed to illustrate where each system was perceived to be in an acoustic space compared to the source and target speakers and to each other. See also item "The Voice Conversion Challenge 2016" (DOI: 10.7488/ds/1430)en_UK
Publisherdc.publisherUniversity of Edinburgh. School of Informatics. Centre for Speech Technology Researchen_UK
Subjectdc.subjectvoice conversionen_UK
Subjectdc.subjectspeaker conversionen_UK
Subject Classificationdc.subject.classificationMass Communications and Documentation::Electronic Media studiesen_UK
Titledc.titleThe Voice Conversion Challenge, 2016: multidimensional scaling (MDS) listening test resultsen_UK

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