Resources for Computer-Based Sign Recognition from Video, and the Criticality of Consistency of Gloss Labeling across Multiple Large ASL Video Corpora

Autor/a: NEIDLE, Carol; OPOKU, Augustine; BALLARD, Carey M.; DAFNIS, Konstantinos M.; CHRONI, Evgenia; METAXAS, Dimitri
Año: 2022
Editorial: European Language Resources Association
Tipo de código: Copyright
Soporte: Digital


Medios de comunicación y acceso a la información » Nuevas Tecnologías, Lingüística » Sistemas de transcripción de las Lenguas de Signos, Lingüística » Corpus signados


The WLASL purports to be “the largest video dataset for Word-Level American Sign Language (ASL) recognition.” It brings together various publicly shared video collections that could be quite valuable for sign recognition research, and it has been used extensively for such research. However, a critical problem with the accompanying annotations has heretofore not been recognized by the authors, nor by those who have exploited these data: There is no 1-1 correspondence between sign productions and gloss labels. Here we describe a large (and recently expanded and enhanced), linguistically annotated, downloadable, video corpus of citation-form ASL signs shared by the American Sign Language Linguistic Research Project (ASLLRP)—with 23,452 sign tokens and an online Sign Bank—in which such correspondences are enforced. We furthermore provide annotations for 19,672 of the WLASL video examples consistent with ASLLRP glossing conventions. For those wishing to use WLASL videos, this provides a set of annotations that makes it possible: (1) to use those data reliably for computational research; and/or (2) to combine the WLASL and ASLLRP datasets, creating a combined resource that is larger and richer than either of those datasets individually, with consistent gloss labeling for all signs. We also offer a summary of our own sign recognition research to date that exploits these data resources.

En Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources.