Automatic Text-Independent Artifact Detection, Localization, and Classification in the Synthetic Speech

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dc.contributor.authorPribil, Jiri
dc.contributor.authorPribilova, Anna
dc.contributor.authorMatousek, Jindrich
dc.coverage.issue4cs
dc.coverage.volume26cs
dc.date.accessioned2018-06-18T10:29:50Z
dc.date.available2018-06-18T10:29:50Z
dc.date.issued2017-12cs
dc.description.abstractThe paper describes experiments with statistical approaches to automatic detection, localization, and classification of the basic types of artifacts in the synthetic speech produced by the Czech text-to-speech system using the unit selection method. The first experiment is aimed at artifact detection by the analysis of variances (ANOVA) and hypothesis testing. The second experiment is focused on localization of the detected artifacts by the Gaussian mixture models (GMM). Finally, the developed open-set artifact classifier is described. The influence of the feature vector length and structure on the resulting artifact detection accuracy is analyzed together with other factors affecting the stability of the artifact detection process. Further investigations have shown a relatively great influence of the number of mixtures and the type of a covariance matrix on the artifact classification error rate as well as on the computational complexity. The obtained experimental results confirm the functionality of the artifact detector based on the ANOVA and hypothesis tests, and the GMM-based artifact localizer and classifier. The described statistical approaches represent the alternatives to the standard listening tests and the manual labeling of the artifacts.en
dc.formattextcs
dc.format.extent1151-1160cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2017 vol. 26, č. 4, s. 1151-1160. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2017.1151en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/82947
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2017/17_01_1151_1160.pdfcs
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.accessopenAccessen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectQuality of synthetic speechen
dc.subjectanalysis of variances (ANOVA)en
dc.subjectGaussian mixture models (GMM) classificationen
dc.subjecttext-to-speech (TTS) systemen
dc.titleAutomatic Text-Independent Artifact Detection, Localization, and Classification in the Synthetic Speechen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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