A machine learning based deaf assistance digital system

The hearing sense is one of the five senses essential for human life. The hearing sense is required to acquire language, learn how to speak, and gain general knowledge. Most importantly, hearing enables individuals to communicate and socialize with the world around them. Deaf people suffer from hearing loss which affects many aspects of their lives; they cannot communicate or socialize effectively with normal people around them. In addition, the safety of deaf people is compromised in many scenarios because they cannot take the right decision emanating from the process of analyzing sounds in the brain, especially alarming sounds. The treatment of hearing loss is dependent on its level. Medicine fails to treat many cases of severe or profound deafness; therefore, people lose hope to live normally. In this paper, we propose a Deaf Assistance Digital System (DADS) that aims to convert major alarm sounds and words which are not distinguishable by deaf people into recognizable alerts for deaf people. The proposed system strives to help deaf people live normally by alerting them about sounds like baby crying or door bell. Moreover, DADS enhances the safety of deaf people by alerting them about alarming sounds and cautionary words. DADS consists of a speech recognition engine and a sound recognition engine. The speech recognition engine is based on the Soundx algorithm while the sound recognition system is implemented using neural networks. The implemented sound recognition and voice recognition engines have more than 90% accuracy.