Year: 2024
Type: article
Abstract: The project is to enable people who are not versedin sign language or people from the deaf or hard- of-hearing community to communicate by using a system that translates their American Sign Language (ASL) gestures into text, which could then be converted into speech. Computer vision and machine learning algorithms allow the system to “read” the sign language as accurately as possible, and then translate into a native text. Text is transcribed to speech using Text-to-Speech (TTS) capabilities The proposed calibration can be applied to real-time applications serving purpose for accessible and decent spoken communication among different individuals with hearing loss which applies the natural co-articulation constraints in various social or professional environments.
Authors: Lisha Kurian, Anaj Pravin, Calvin Johnson, Abhishek Unnikrishnan, Aswin Sunil
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: diamond
OpenAlex ID: W4404729365