Machine Learning & Hearing Loss

Machine Learning & Hearing Loss

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.

Papers (In order)

All of these papers are accessible without any university sponsorship or payment.

  1. Why aren’t better assistive technologies available for those communicating using ASL?
  2. Grammatical Facial Expressions Recognition with Machine Learning
  3. A Machine Learning Approach to Fitting Prescription for Hearing Aids
  4. AudioVision: Sound Detection for the Deaf and Hard-of-hearing

Gloves that talk

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

Interpreting issues

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.

Image for post

Photo by Franck V. on Unsplash

A possible solution

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).

A long way to go

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.

machine-learning nlp neural-networks deaf artificial-intelligence

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

AI(Artificial Intelligence): The Business Benefits of Machine Learning

Enroll now at CETPA, the best Institute in India for Artificial Intelligence Online Training Course and Certification for students & working professionals & avail 50% instant discount.

Learning in Artificial Intelligence - Great Learning

What is Artificial Intelligence (AI)? AI is the ability of a machine to think like human, learn and perform tasks like a human. Know the future of AI, Examples of AI and who provides the course of Artificial Intelligence?

Fundamentals of Neural Network in Machine Learning

Fundamentals of Neural Network in Machine Learning. What is a Neuron? What is the Activation Function? How do Neural Network Works? How do Neural Networks Learn?

Introduction to Artificial Neural Networks for Beginners

Introduction to Artificial Neural Networks for Beginners. Understanding the concepts of Neural Networks.

Quantum Machine Learning: learning on neural networks

Quantum Machine Learning: learning on neural networks. Analytical gradient computation, the Hadamard test, and more. This time, we’re going a little deeper into the rabbit hole and looking at how to build a neural network on a quantum computer.