Paper Title
IMPACT OF DEMONSTRATION VARIABILITY ON DEEP NEURAL NETWORK PERFORMANCE IN SIGN LANGUAGE RECOGNITION

Abstract
This article investigates the impact of demonstration variability on the accuracy of recognizing sign language words and alphabet using deep neural networks. The primary focus is on evaluating the effectiveness of various deep learning architectures. The key contribution of this work lies in identifying the importance of multimodal approaches and model adaptation to enhance the accuracy of gesture recognition under conditions of low variability, specifically using the alphabet as an example. This is crucial for the development of automatic sign language and alphabet recognition systems. It is particularly critical because, unlike dynamic words, proper nouns demonstrated through the alphabet must be recognized with consistent efficiency during continuous recognition. Keywords - gesture recognition, variability, lip reading, multimodal approaches, clustering, model adaptation, real-time recognition