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dc.contributor.advisorNavas Cordón, Eva ORCID
dc.contributor.advisorSaratxaga Couceiro, Ibon ORCID
dc.contributor.authorDe Zuazo Oteiza, Xabier
dc.date.accessioned2023-06-30T14:44:38Z
dc.date.available2023-06-30T14:44:38Z
dc.date.issued2023-06-30
dc.identifier.urihttp://hdl.handle.net/10810/61815
dc.description.abstract[EN] Lately, multiple Text-to-Speech models have emerged using Deep Neural networks to synthesize audio from text. In this work, the state-of-the-art multilingual and multi-speaker Text-to-Speech model has been trained in Basque, Spanish, Catalan, and Galician. The research consisted of gathering the datasets, pre-processing their audio and text data, training the model in the languages in different steps, and evaluating the results at each point. For the training step, a transfer learning approach has been used from a model already trained in three languages: English, Portuguese, and French. Therefore, the final model created here supports a total of seven languages. Moreover, these models also support zero-shot voice conversion, using an input audio file as a reference. Finally, a prototype application has been created to do Speech-to-Speech Translation, putting together the models trained here and other models from the community. Along the way, some Deep Speech Speech-to-Text models have been generated for Basque and Galician.es_ES
dc.description.abstract[EU] Azkenaldian, Text-to-Speech eredu anitz sortu dira sare neuronal sakonak erabiliz, testutik audioa sintetizatzeko. Lan honetan, state-of-the-art Text-to-Speech eredu eleaniztun eta hiztun anitzeko eredua landu da euskaraz, gaztelaniaz, katalanez eta galegoz. Ikerketa honetan datu-multzoak bildu, haien audio- eta testu-datuak aldez aurretik prozesatu, eredua hizkuntzetan entrenatu da urrats desberdinetan eta emaitzak puntu bakoitzean ebaluatu dira. Entrenatze-urratserako, ikaskuntza-transferentzia teknika erabili da dagoeneko hiru hizkuntzatan trebatutako eredu batetik abiatuta: ingelesa, portugesa eta frantsesa. Beraz, hemen sortutako azken ereduak zazpi hizkuntza onartzen ditu guztira. Gainera, eredu hauek zero-shot ahots bihurketa ere egiten dute, sarrerako audio fitxategi bat erreferentzia gisa erabiliz. Azkenik, Speech-to-Speech Translation egiteko prototipo aplikazio bat sortu da hemen entrenatutako ereduak eta komunitateko beste eredu batzuk elkartuz. Bide horretan, Deep Speech Speech-to-Text eredu batzuk sortu dira euskararako eta galegorako.es_ES
dc.language.isoenges_ES
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.subjectmultilingual multi-speaker text-to-speeches_ES
dc.subjectspeech-to-textes_ES
dc.subjectmachine translationes_ES
dc.subjectspeech-to-speech translationes_ES
dc.subjectcross-lingual zero-shot voice conversiones_ES
dc.subjectBasquees_ES
dc.subjectSpanishes_ES
dc.titleBasque and Spanish Multilingual TTS Model for Speech-to-Speech Translationes_ES
dc.typeinfo:eu-repo/semantics/masterThesis
dc.date.updated2023-02-09T11:17:24Z
dc.language.rfc3066es
dc.rights.holderAttribution-ShareAlike 4.0 International (CC BY-SA 4.0)
dc.contributor.degreeMáster Universitario en Análisis y Procesamiento del Lenguaje
dc.contributor.degreeHizkuntzaren Azterketa eta Prozesamendua Unibertsitate Masterra
dc.identifier.gaurregister128824-383364-05es_ES
dc.identifier.gaurassign142572-383364es_ES


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Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)