Bangla Text Reading Facility For Unprivileged People Using Deep CNN

dc.contributor.advisorDr. Md. Shahriar Karim
dc.contributor.authorDip Roy
dc.contributor.authorAshiful Alam Shuvo
dc.contributor.authorA.B.M Kaish Ibna Sufian
dc.contributor.id1912355042
dc.contributor.id1731435642
dc.contributor.id1731253042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025
dc.date.accessioned2025-07-21T04:12:39Z
dc.date.available2025-07-21T04:12:39Z
dc.date.issued2023
dc.description.abstractBengali Text-to-Speech (TTS) is the term used to describe the process of turning written Bengali text, including sentences, paragraphs, and documents, into spoken Bengali speech. Creating artificial human voices from text is referred to as text-to-speech. TTS systems are capable of producing voices that sound like humans. In this study, we provide a unique Bengali text-to-speech (TTS) method based on deep convolutional neural networks (CNN) without recurrent components. For our Text to Voice System (TTS) for Bengali Language, our primary strategy is to provide a high-quality Bengali voice and audio processing unit. The goal of this work is to show how a different neural TTS that just employs CNN can lower these training expenses. It took a long time to train the suggested Deep Convolutional TTS. Our major objective is to teach the model how to read Bengali text and produce voice automatically.
dc.description.degreeUndergraduate
dc.identifier.cd600000192
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1293
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSU Library
dc.titleBangla Text Reading Facility For Unprivileged People Using Deep CNN
oaire.citation.endPage43
oaire.citation.startPage1
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