A Spatio-Temporal Representation Learning as an Alternative to Traditional Glosses in Sign Language Translation and Production

• URL: http://arxiv.org/abs/2407.02854v2
• Date: Wed, 04 Dec 2024 13:41:11 GMT
• Title: A Spatio-Temporal Representation Learning as an Alternative to Traditional Glosses in Sign Language Translation and Production
• Authors: Eui Jun Hwang, Sukmin Cho, Huije Lee, Youngwoo Yoon, Jong C. Park,
• Abstract summary: This paper addresses the challenges associated with the use of glosses in Sign Language Translation (SLT) and Sign Language Production Language (SLP)<n>We introduce Universal Gloss-level Representation (UniGloR), a framework designed to capture thetemporal inherent sign language.<n>Our experiments in a keypoint-based setting demonstrate that UniGloR either outperforms or matches performance of previous SLT and SLP methods.
• Score: 9.065171626657818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work addresses the challenges associated with the use of glosses in both Sign Language Translation (SLT) and Sign Language Production (SLP). While glosses have long been used as a bridge between sign language and spoken language, they come with two major limitations that impede the advancement of sign language systems. First, annotating the glosses is a labor-intensive and time-consuming process, which limits the scalability of datasets. Second, the glosses oversimplify sign language by stripping away its spatio-temporal dynamics, reducing complex signs to basic labels and missing the subtle movements essential for precise interpretation. To address these limitations, we introduce Universal Gloss-level Representation (UniGloR), a framework designed to capture the spatio-temporal features inherent in sign language, providing a more dynamic and detailed alternative to the use of the glosses. The core idea of UniGloR is simple yet effective: We derive dense spatio-temporal representations from sign keypoint sequences using self-supervised learning and seamlessly integrate them into SLT and SLP tasks. Our experiments in a keypoint-based setting demonstrate that UniGloR either outperforms or matches the performance of previous SLT and SLP methods on two widely-used datasets: PHOENIX14T and How2Sign.

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