LSA-T: The first continuous Argentinian Sign Language dataset for Sign Language Translation
This work addresses a data gap for Argentinian Sign Language translation, which could aid integration of deaf people, but is incremental as it focuses on dataset creation rather than novel methods.
The authors tackled the lack of continuous Argentinian Sign Language (LSA) data by creating the first such dataset, containing 14,880 sentence-level videos with annotations, and provided a baseline neural model for sign language translation.
Sign language translation (SLT) is an active field of study that encompasses human-computer interaction, computer vision, natural language processing and machine learning. Progress on this field could lead to higher levels of integration of deaf people. This paper presents, to the best of our knowledge, the first continuous Argentinian Sign Language (LSA) dataset. It contains 14,880 sentence level videos of LSA extracted from the CN Sordos YouTube channel with labels and keypoints annotations for each signer. We also present a method for inferring the active signer, a detailed analysis of the characteristics of the dataset, a visualization tool to explore the dataset and a neural SLT model to serve as baseline for future experiments.