A brief literature review of how machine learning benefits individuals with hearing loss. Machine learning (ML) has spread into many different fields and disciplines. Dipping your toes into a new field is the best way to grow and learn new things.
Machine learning (ML) has spread into many different fields and disciplines. Dipping your toes into a new field is the best way to grow and learn new things. The following is a summary of how researchers have applied machine learning to improve the lives of those who are deaf and hard of hearing.
All of these papers are accessible without any university sponsorship or payment.
This article by Keith Kirkpatrick introduces problems that deaf and hard of hearing communities have when talking with people who do not know sign language.
Robotics, NLP, ASL, Wearables
Hard of hearing individuals rely on interpreting services, either in person or online, to interact with the hearing world at the doctor's office, courtroom, or coffee shop. However, these services are not always available and online interpreting is plagued with the problems of mobile internet: slow, inconsistent, or non-existent.
A solution to the lack of interpreters are gloves that can translate American Sign Language (ASL) to English. With motion sensors embedded the glove record the user's motions and translate the motion into the correct sign. Several different ML algorithms can be used to find the correct sign: K-means, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN).
SignAloud and BrightSign are two companies highlighted in the article. BrightSign is recognized as superior to SignAloud because users can record their own versions of signs for better translation. However, both of these products fall short of real interpretation because they do not consider facial expressions. Facial expressions are a huge part of ASL and a lot of meaning can be lost if they are not considered. This is why you see ASL interpreters taking off their masks while interpreting for officials.
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