International Journal of Applied Information Systems |
Foundation of Computer Science (FCS), NY, USA |
Volume 12 - Number 27 |
Year of Publication: 2020 |
Authors: Afif Bin Kamrul, Shayla Sharmin |
10.5120/ijais2020451841 |
Afif Bin Kamrul, Shayla Sharmin . An Android Communication Platform between Hearing Impaired and General People. International Journal of Applied Information Systems. 12, 27 ( January 2020), 1-6. DOI=10.5120/ijais2020451841
An enormous number of deaf and mute people in society use sign language to communicate. Besides, the general people also use both verbal and nonverbal language to establish a perfect communication system. Although the general people use several signs in everyday life there is a large gap between the general and the deaf and mute people in terms of communication as there are many special signs that are only used by the heard impaired people. Again, some signs are common all over the world but most of the signs used by the hearing impaired people are differed by place to place. Smartphones can be a preferable technology to develop a mechanism that will help these two communities to make effective communication among them, as the use of smartphones is now at peak. In this work, an android-based application has been established which helps to build a connection between general and hearing-impaired people who speak Bangla as smartphone is now very popular among the users and it is easy to use. The system implies a Bangla voice recognition system for general people through which they can input their voice in their application. There are more than 200 Bangla words available in the application. Whenever voice is detected, the words are separated and translated into sign language animation and played sequentially. On the contrary, there is a keyboard for deaf/mute users who can use this to express their language readable to general users. The project has been tested in real life scenarios to evaluate the project for real-life purposes. The test results shows that the developed sign language convert module gives accuracy of 88% and the keyboard module gives satisfactory results as well.