Maria Naseem1, Summaira Sarfraz, Ali, Husnain Raza.
The present research aims to evaluate the effectiveness of the developed prototype, which translates Pakistan sign language into text and speech while using convolutional neural networking. Due to the lack of sign language teaching, unimpaired teachers face problems while teaching deaf students. The development of a translation tool can fill this communication gap. Research indicates that the gap was highlighted and to bridge the communication gap among impaired and impaired people, ta prototype was conceived, which can translate the Pakistan sign language into English text and speech. The study employed a qualitative approach. Eight teachers were selected as sample using purposive sampling technique who were teaching deaf students. The present research incorporates the practical application of the sign translation tool in the separate article. Data was collected through focus group interviews. Data were analyzed using Nvivo. Findings of the results revealed that the tool proved to be effective in overcoming the communication barrier between deaf learners and unimpaired teachers. Moreover, the sign to speech translation option is more effective because it facilitates visually blind learners. This tool helps the teachers and other people to teach and communicate with deaf people, efficiently and quickly.
Received Date: 2019-11-25