Authors :
Louis Zendrix C. Adornado; Daniella Kite V. Latorre; Aldus Irving B. Serrano; Mohammad Elyjah K. Masukat; Lawrence Kristopher A. Lontoc; Julie Ann B. Real
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/mstm8zuj
Scribd :
https://tinyurl.com/4sz9kw27
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR744
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
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.
Keywords :
Artificial Intelligence, Assistive Technology, Sign Language Translator, Speech-Impaired, TensorFlow, TensorFlow Action Recognition.
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.
Keywords :
Artificial Intelligence, Assistive Technology, Sign Language Translator, Speech-Impaired, TensorFlow, TensorFlow Action Recognition.
