Speech Recognition and Transformation in Sign Language Using CNN

Sign language is a visual language that uses a combination of hand gestures, facial expressions and body language to communicate. It is used by people who are deaf or hard of hearing to communicate with each other. Convolutional neural networks (CNNs) will be used in this research to create a system that can identify speech and translate it into sign language motions. The suggested solution comprises recording audio input, processing it using a speech recognition algorithm and then utilizing a trained CNN model to map the identified words to match sign language motions. To precisely map spoken words to their corresponding gestures, a large dataset of sign language videos is used to train the CNN model.