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Please use this identifier to cite or link to this item: https://dspace.univ-ouargla.dz/jspui/handle/123456789/36703
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dc.contributor.authorBETTAYEB, Nadjla-
dc.contributor.authorBenettouati, Salah Eddine-
dc.contributor.authorBoukhetta, Mottaz Bellah-
dc.contributor.authorBouzid, Mohamed laid-
dc.date.accessioned2024-09-17T10:00:41Z-
dc.date.available2024-09-17T10:00:41Z-
dc.date.issued2024-
dc.identifier.citationFACULTE DES NOUVELLES TECHNOLOGIES DE L'INFORMATIQUE ET DE LA COMMUNICATIONen_US
dc.identifier.urihttps://dspace.univ-ouargla.dz/jspui/handle/123456789/36703-
dc.description.abstractThis thesis aims to build an Arabic Text To Speech (TTS) system using the deep learning model Tacotron. The first stage consists of training an Artificial Neural Network (ANN) model able to generate audio spectrograms from a given text. The second stage transforms the generated Spectrogram into speech, using the HiFi GAN audio enhancement model. The desired goals were fully reached, as the synthesized Arabic speech received a general rate of 3.66 over 5, from the system evaluatorsen_US
dc.language.isoenen_US
dc.publisherUNIVERSITY KASDI MERBAH OUARGLAen_US
dc.subjectTacotronen_US
dc.subjectTTSen_US
dc.subjectDeep Learningen_US
dc.subjectANNen_US
dc.subjectArabic languageen_US
dc.titleAn Arabic Text To Speech Synthesis system based on Tacotron modelen_US
dc.typeThesisen_US
Appears in Collections:Département d'Electronique et des Télécommunications - Master

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