Active convolutional neural networks sign language (ActiveCNN-SL) framework: a paradigm shift in deaf-mute communication

Abstract

Abstract Real-time speech-to-text and text-to-speech technologies have significantly influenced the accessibility of communication for individuals who are deaf or mute. This research aims to assess the efficacy of these technologies in facilitating communication between deaf or mute individuals and those who are neither deaf nor mute. A mixed-method approach will incorporate qualitative and quantitative data collection and analysis techniques. The study will involve participants from deaf or mute and non-deaf or non-mute communities. The research will scrutinize the precision and efficiency of communication using these technologies and evaluate user experience and satisfaction. Furthermore, the study intends to pinpoint potential obstacles and limitations of these technologies and offer suggestions for enhancing their effectiveness in fostering inclusivity. The study proposes an active learning framework for sign language gesture recognition, termed Active Convolutional Neural Networks—Sign Language (ActiveCNN-SL). ActiveCNN-SL aims to minimize the labeled data required for training and augment the accuracy of sign language gesture recognition through iterative human feedback. This proposed framework holds the potential to enhance communication accessibility for deaf and mute individuals and encourage inclusivity across various environments. The proposed framework is trained using two primary datasets: (i) the Sign Language Gesture Images Dataset and (ii) the American Sign Language Letters (ASL)—v1. The framework employs Resnet50 and YoloV.8 to train the datasets. It has demonstrated high performance in terms of precision and accuracy. The ResNet model achieved a remarkable accuracy rate of 99.98% during training, and it also exhibited a validation accuracy of 100%, surpassing the baseline CNN and RNN models. The YOLOv8 model outperformed previous methods on the ASL alphabet dataset, achieving an overall mean average accuracy for all classes of 97.8%.