Embedded Deep Learning to Support Hearing Loss Mobility: In-House Speaking Assistant
Date Issued
2022
Author(s)
Stanojkovski, Nikola
Pavlov, Tashko
Stojchevski, Mario
Simjanoska, Monika
Abstract
Hearing-impaired people encounter significant mobility issues in their everyday life as the desire to communicate what they think
and need in an increasingly crowded urban society grows, all for social,
physiological, safety, and self-esteem reasons. Because of the rapid advancement of technology in the twenty-first century, there are several
tools available to aid these individuals with their difficulties and to enable them to clarify their thoughts through a number of devices. All of
these technologies, however, are prohibitively expensive and difficult to
obtain for these individuals. Many of these assistants require an Internet
connection to function properly since they involve extensive data processing on certain Cloud services, which can be a limiting factor because
Internet connections are not available everywhere, and not everyone has
easy access to them. In this study, we offer a methodology for providing
a hearing-impaired person with a speaking assistant that is entirely integrated within the device and does not require an Internet connection,
making it a highly economical and portable solution for anyone. Our
solution embeds one intelligent Deep Learning model, a text-to-speech
model, on a single smart mobile device. This model generates an audio
file from the text entered by the user and plays it back to the device’s
available output speaker. Our work brings us one step closer to fully
self-contained embedded intelligent models that use cutting-edge AI to
assist hearing-impaired people with communication.
and need in an increasingly crowded urban society grows, all for social,
physiological, safety, and self-esteem reasons. Because of the rapid advancement of technology in the twenty-first century, there are several
tools available to aid these individuals with their difficulties and to enable them to clarify their thoughts through a number of devices. All of
these technologies, however, are prohibitively expensive and difficult to
obtain for these individuals. Many of these assistants require an Internet
connection to function properly since they involve extensive data processing on certain Cloud services, which can be a limiting factor because
Internet connections are not available everywhere, and not everyone has
easy access to them. In this study, we offer a methodology for providing
a hearing-impaired person with a speaking assistant that is entirely integrated within the device and does not require an Internet connection,
making it a highly economical and portable solution for anyone. Our
solution embeds one intelligent Deep Learning model, a text-to-speech
model, on a single smart mobile device. This model generates an audio
file from the text entered by the user and plays it back to the device’s
available output speaker. Our work brings us one step closer to fully
self-contained embedded intelligent models that use cutting-edge AI to
assist hearing-impaired people with communication.
Subjects
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