Gesture recognition is an important technique for several types of applications, including human–computer interfaces, sign language interpretation, and interactive gaming. In this study, we investigate how noise can enhance context-aware, interpretable gesture recognition by adding noise to the recognition process so as to make gesture inputs robust and contextually accurate. Several deep learning and deep learning models, including Convolutional Neural Networks, VGG19, ResNet50, VGG16, and Vision Transformer, were trained on the Hagrid dataset, and important features such as motion patterns, hand shapes, hand region, and hand pose were extracted from the gesture data. The models achieved accuracy rates of 94%, 83%, 79%, 81%, and 77%, respectively. Layer-wise Relevance Propagation, Grad-CAM, Grad-CAM++, and Score-CAM are then used to explain the models’ decision-making process, revealing how noise can increase contextual awareness and gesture interpretation. These techniques work by highlighting regions in the visualised image that are most relevant to its decision, thereby enhancing the reliability of gesture recognition models and also building trust and usability in real-world applications. This signifies a critical step towards building responsible software systems for sign language translation.