The use of TensorFlow Action Recognition as the Main Component in Making a Sign Language Translator Speaker for Speech-Impaired People

Abstract

Due to communication barriers, deaf and mute students are separated from their friends, families and communities as their schools do not offer sign language instruction. Consequently, this cluster of people may feel excluded from their communities, depriving them the chance of living a normal life that is free from discrimination. The objective of this quantitative experimental study is to use TensorFlow Action Recognition as the main component in making a Sign Language Translator Speaker for Speech-Impaired People. Based on the results, the device can successfully translate sign languages with an average of 5.91 seconds, and translate three signs per 30 seconds. Also, it was found that it can detect distances up to four meters. The study manifested that the device provides the service of breaking past the communication barriers to the speech- impaired and hearing-impaired individuals, which advocates and facilitates effective communication while fostering inclusivity. These results affirmed that it is feasible to make a Sign Language Translator Speaker with the use of TensorFlow Action Recognition. Thus, this Sign Language Speaker device offers the best services for deaf and mute people Qatar and all around the world, as the struggles of hearing and speech- impaired people can be alleviated.