This project is called “Bridge: an AI powered sign language interpreter”. The software's primary function is to translate American Sign Language into written or spoken English, targeting individuals with hearing or speech impairments. These individuals often face communication challenges when interacting with people who use verbal communication. Such barriers often limit their opportunities. Thus, this project aimed to bridge this communication gap and promote efficient interaction. Throughout the project, I employed cutting-edge technology and relevant research to guide the development process, therefore achieving the ultimate goal of designing and developing a prototype that translates sign language accurately and in a user-friendly way.
This project mainly aims to aid those who use sign language on a day-to-day basis. Despite having 72 million people around the globe using sign language, language barriers are still present as people would only learn sign language if they or a person they know has some form of disability. These language barriers often lead to a lack of access to opportunity or proper education. An established solution to this is often through hiring translators or interpreters to facilitate a conversation, but this is often costly or difficult to attain given the small amount of people that have mastered sign language.
With the emergence of new technologies such as machine learning and artificial intelligence, new methods for aiding the hearing or speech impaired are becoming more feasible. Currently, technological advances in computer vision and machine learning are paving the way to create systems that understand human movement in the virtual space. Some projects have even started to explore different ways to translate sign language into written speech. This is where my study comes in. I aim to add onto this continuous quest to finding efficient ways to bridge gaps in communication for sign language users.
Currently there are limited products in doing this. Mainly because of the scale and complexity of language. Several solutions which have the same goal try to use different methods to interface body movements into language digitally. The technology behind this is often complicated, making access to it difficult. This is why I mainly want to find ways to create an open source and easily to access system for interpreting while being accurate and user friendly.
The following are photos of the features of Bridge and how to use it.
Bridge has four fundamental features:
(1) Signing - A person can simply sign in front of their webcams and some options show up underneath their video feed
(2) Choosing options - A user can point their finger over the option they want to choose after signing the gesture
(3) Sentence building - The words chosen will show up in the “you” speech bubble so it can represent the sentence that the user wants to say
