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dc.contributor.authorAlonso, Agustin
dc.contributor.authorGarcía Romillo, Víctor
dc.contributor.authorHernáez Rioja, Inmaculada ORCID
dc.contributor.authorNavas Cordón, Eva ORCID
dc.contributor.authorSánchez de la Fuente, Jon ORCID
dc.date.accessioned2022-03-14T09:11:34Z
dc.date.available2022-03-14T09:11:34Z
dc.date.issued2022-02-27
dc.identifier.citationApplied Sciences 12 5) : (2022) // Article ID 2473es_ES
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10810/55917
dc.description.abstractSpeech is the most common way of communication among humans. People who cannot communicate through speech due to partial of total loss of the voice can benefit from Alternative and Augmentative Communication devices and Text to Speech technology. One problem of using these technologies is that the included synthetic voices might be impersonal and badly adapted to the user in terms of age, accent or even gender. In this context, the use of synthetic voices from voice banking systems is an attractive alternative. New voices can be obtained applying adaptation techniques using recordings from people with healthy voice (donors) or from the user himself/herself before losing his/her own voice. In this way, the goal is to offer a wide voice catalog to potential users. However, as there is no control over the recording or the adaptation processes, some method to control the final quality of the voice is needed. We present the work developed to automatically select the best synthetic voices using a set of objective measures and a subjective Mean Opinion Score evaluation. A prediction algorithm of the MOS has been build which correlates similarly to the most correlated individual measure.es_ES
dc.description.sponsorshipThis work has been funded by the Basque Government under the project ref. PIBA 2018-035 and IT-1355-19. This work is part of the project Grant PID 2019-108040RB-C21 funded by MCIN/AEI/10.13039/501100011033.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID 2019-108040RB-C21es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectSTOIes_ES
dc.subjectESTOIes_ES
dc.subjectNISQAes_ES
dc.subjectSIIBes_ES
dc.subjectspeech adaptationes_ES
dc.subjectvoice bankies_ES
dc.titleAutomatic Classification of Synthetic Voices for Voice Banking Using Objective Measureses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2022-03-10T14:18:36Z
dc.rights.holder2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2076-3417/12/5/2473/htmes_ES
dc.identifier.doi10.3390/app12052473
dc.departamentoesIngeniería de comunicaciones
dc.departamentoeuKomunikazioen ingeniaritza


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2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).