Silent Speaker: A Lip-reading Model Using Deep Learning

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dc.contributor.advisorMohammad Ashrafuzzaman Khan
dc.contributor.authorFaria Karim Porna
dc.contributor.authorNazmul Hauqe
dc.contributor.id1620424042
dc.contributor.id1530862642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-05-15
dc.date.accessioned2024-05-15T04:52:02Z
dc.date.available2024-05-15T04:52:02Z
dc.date.issued2020
dc.description.abstractSilent Speaker is an applied model of human-computer interaction. This model can be applied in various vital areas, such as crime fighting and helping the hearing-impaired. It consists of one subject: Speech Recognition. This project is done to recognize speech without the presence or support of any auditory signal. So far, a lot of research has been done on lip-reading in English, French, Chinese, and many other languages. However, little research has been done to recognize speech from a silent video in the Bengali language. This thesis work provides a new approach to detecting some words in the Bengali language using deep learning. We want to classify some words using the dataset we created for this project. We track the distance between the inner and outer lip extract features for LSTM and create our model that can classify the word. Our final accuracy is 43%, and in the future, we want to increase our dataset size and modify our model to produce more accuracy.
dc.description.degreeUndergraduate
dc.identifier.cd600000333
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/725
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSUlibrary
dc.titleSilent Speaker: A Lip-reading Model Using Deep Learning
dc.typeProject
oaire.citation.endPage51
oaire.citation.startPage1
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