The How2Sign is a multimodal and multiview continuous American Sign Language (ASL) dataset consisting of a parallel corpus of more than 80 hours of sign language videos and a set of corresponding modalities including speech, English transcripts, and depth. A three-hour subset was further recorded in the Panoptic studio enabling detailed 3D pose estimation.
28 PAPERS • 3 BENCHMARKS
YouTube-ASL is 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.
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An artificial corpus built using grammatical dependencies rules due to the lack of resources for Sign Language.
1 PAPER • 1 BENCHMARK
The ASL-Phono introduces a novel linguistics-based representation, which describes the signs in the ASLLVD dataset in terms of a set of attributes of the American Sign Language phonology.
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The ASL-Skeleton3D introduces a representation based on mapping into the three-dimensional space the coordinates of the signers in the ASLLVD dataset. This enables a more accurate observation of the body parts and the signs articulation, allowing researchers to better understand the language and explore other approaches to the SLR field.
Extremely important: The ASLLVD video data are subject to Terms of Use: http://www.bu.edu/asllrp/signbank-terms.pdf. By downloading these video files, you are agreeing to respect these conditions. In particular, NO FURTHER REDISTRIBUTION OF THESE VIDEO FILES is allowed.