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YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus

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arxiv 2306.15162 v2 pith:VKGOPENV submitted 2023-06-27 cs.CL cs.CV

David Uthus , Garrett Tanzer , Manfred Georg This is my paper · ORCID
classification cs.CLcs.CV
keywords youtube-aslsignamericancorpusenglishlarge-scaleopen-domainsigners
verification ladder T0 review T1 audit T2 compute T3 formal
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Machine learning for sign languages is bottlenecked by data. In this paper, we present YouTube-ASL, a large-scale, open-domain corpus of American Sign Language (ASL) videos and accompanying English captions drawn from YouTube. With ~1000 hours of videos and >2500 unique signers, YouTube-ASL is ~3x as large and has ~10x as many unique signers as the largest prior ASL dataset. We train baseline models for ASL to English translation on YouTube-ASL and evaluate them on How2Sign, where we achieve a new finetuned state of the art of 12.39 BLEU and, for the first time, report zero-shot results.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards AI-driven Sign Language Generation with Non-manual Markers

    cs.HC 2025-02 conditional novelty 6.0 of 10

    The authors combine an LLM, motion matching, and a pose-to-video model to generate ASL videos with non-manual markers, reporting a BLEU-4 of 0.276 for text-to-gloss and a user study where DHH participants rated genera...

  2. AzSLD: Azerbaijani Sign Language Dataset for Fingerspelling, Word, and Sentence Translation with Baseline Software

    cs.CL 2024-11 conditional novelty 6.0 of 10

    AzSLD, a new public Azerbaijani Sign Language dataset with fingerspelling, 100 word classes, 500 sentence videos from two camera views, and a data loader, is introduced.

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