Abstract
Machine learning models fundamentally rely on large quantities of high-quality data. Collecting the necessary data for these models can be challenging due to cost, scarcity, and privacy restrictions. Signed languages are visual languages used by the deaf community and are considered low-resource languages. Sign language datasets are often orders of magnitude smaller than their spoken language counterparts. Sign Language Production (SLP) is the task of generating sign language videos from spoken language sentences, while Sign Language Translation (SLT) is the reverse translation task. Here, we propose leveraging recent advancements in SLP to augment existing sign language datasets and enhance the performance of SLT models. For this, we utilize three techniques: a skeleton-based approach to production, sign stitching, and two photo-realistic generative models, SignGAN and SignSplat. We evaluate the effectiveness of these techniques in enhancing the performance of SLT models by generating variation in the signer's appearance and the motion of the skeletal data. Our results demonstrate that the proposed methods can effectively augment existing datasets and enhance the performance of SLT models by up to 19%, paving the way for more robust and accurate SLT systems, even in resource-constrained environments.