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A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute
by Muhammad Imran Saleem 1,2,*ORCID,Atif Siddiqui 3ORCID,Shaheena Noor 4ORCID,Miguel-Angel Luque-Nieto 1,2ORCID andPablo Otero 1,2ORCID
1
Telecommunications Engineering School, University of Malaga, 29010 Malaga, Spain
2
Institute of Oceanic Engineering Research, University of Malaga, 29010 Malaga, Spain
3
Airbus Defence and Space, UK
4
Department of Computer Engineering, Faculty of Engineering, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(1), 453; https://doi.org/10.3390/app13010453
Received: 12 November 2022 / Revised: 22 December 2022 / Accepted: 26 December 2022 / Published: 29 December 2022
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Abstract
Deaf and mute people are an integral part of society, and it is particularly important to provide them with a platform to be able to communicate without the need for any training or learning. These people rely on sign language, but for effective communication, it is expected that others can understand sign language. Learning sign language is a challenge for those with no impairment. Another challenge is to have a system in which hand gestures of different languages are supported. In this manuscript, a system is presented that provides communication between deaf and mute (DnM) and non-deaf and mute (NDnM). The hand gestures of DnM people are acquired and processed using deep learning, and multiple language support is achieved using supervised machine learning. The NDnM people are provided with an audio interface where the hand gestures are converted into speech and generated through the sound card interface of the computer. Speech from NDnM people is acquired using microphone input and converted into text. The system is easy to use and low cost. (...)
