Abstract
Sign Language conveys information through multiple channels composed of manual (handshape, hand movement) and non-manual (facial expression, mouthing, body posture) components. Sign language assessment involves giving granular feedback to a learner, in terms of correctness of the manual and non-manual components, aiding the learner's progress. Existing methods rely on handcrafted skeleton-based features for hand movement within a KL-HMM framework to identify errors in manual components. However, modern deep learning models offer powerful spatio-temporal representations for videos to represent hand movement and facial expressions. Despite their success in classification tasks, these representations often struggle to attribute errors to specific sources, such as incorrect handshape, improper movement, or incorrect facial expressions. To address this limitation, we leverage and analyze the spatio-temporal representations from Inflated 3D Convolutional Networks (I3D) and integrate them into the KL-HMM framework to assess sign language videos on both manual and non-manual components. By applying masking and cropping techniques, we isolate and evaluate distinct channels of hand movement, and facial expressions using the I3D model and handshape using the CNN-based model. Our approach outperforms traditional methods based on handcrafted features, as validated through experiments on the SMILE-DSGS dataset, and therefore demonstrates that it can enhance the effectiveness of sign language learning tools.