Bangla Text Reading Facility For Unprivileged People Using Deep CNN
dc.contributor.advisor | Dr. Md. Shahriar Karim | |
dc.contributor.author | Dip Roy | |
dc.contributor.author | Ashiful Alam Shuvo | |
dc.contributor.author | A.B.M Kaish Ibna Sufian | |
dc.contributor.id | 1912355042 | |
dc.contributor.id | 1731435642 | |
dc.contributor.id | 1731253042 | |
dc.coverage.department | Electrical and Computer Engineering | |
dc.date.accessioned | 2025 | |
dc.date.accessioned | 2025-07-21T04:12:39Z | |
dc.date.available | 2025-07-21T04:12:39Z | |
dc.date.issued | 2023 | |
dc.description.abstract | Bengali 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.degree | Undergraduate | |
dc.identifier.cd | 600000192 | |
dc.identifier.print-thesis | To be assigned | |
dc.identifier.uri | https://repository.northsouth.edu/handle/123456789/1293 | |
dc.language.iso | en | |
dc.publisher | North South University | |
dc.rights | ©NSU Library | |
dc.title | Bangla Text Reading Facility For Unprivileged People Using Deep CNN | |
oaire.citation.endPage | 43 | |
oaire.citation.startPage | 1 |
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