Text to Speech and Language Conversion in Hindi and English Using CNN

Abstract

Abstract: Deaf and dumb persons use sign language to communicate with other people in their society. Because sign language is the only means of communication for persons who are deaf or hard of hearing, it is mostly utilized by them. Ordinary folks are unfamiliar with this language. A real-time sign language recognition system has been developed in this article to allow those who do not know sign language to communicate with hearing-impaired people more readily. In this case, we employed American Sign Language. We have used American Sign Language in this paper. We introducing the development and implementation of an American Sign Language (ASL) derived from convolutional neural network. Deep Learning Method is used to train a classifier to recognize Sign Language and Convolutional Neural Network (CNN) is used to extract features from the images. We have also used Text-To-Speech Synthesis to convert the detected output into speech format. With use of MATLAB function the obtained text is converted into voice. In our system we are converting text to speech in Hindi language. Therefore hand gesture made by deaf and dumb people has been anatomized and restated into text and voice for better communication. Keywords: Sign Language Recognition, Image Processing, Text-To-Speech, American Sign Language, Classification, Convolutional Neural Network, Deep Learning real-time