Publication

Abstract : In this rapidly changing world where everyone is vying for recognition, opportunities, success, and survival, people with disabilities frequently find themselves marginalized. They struggle to blend in with society and receive different treatment. The advancement of AI-assisted technologies has contributed to closing this social gap and given the disabled newfound hope. The "Sign-to-Speech" glove makes it possible to translate sign language into speech. Flex sensors that track finger movement are integrated into the glove, and an Arduino microcontroller is used to process the sensor data. The speech synthesis software is run on a Raspberry Pi, which is connected to the Arduino. The way in which the glove functions is by tracking finger and hand movements that create various sign language expressions. The Arduino receives data from the flex sensors, which track variations in resistance as the fingers move and acts as an Analog-to-Digital converter. The machine learning model is deployed in the Raspberry Pi, which is used to identify the sigh and convert it into speech. The speech output is routed through a speaker, hence making it possible for the listener to hear it. By providing pertinent data to the machine learning model for training, the device can be configured for various sign languages making the overall system versatile.