TY - GEN
T1 - Resources for Computer-Based Sign Recognition from Video, and the Criticality of Consistency of Gloss Labeling across Multiple Large ASL Video Corpora
AU - Neidle, Carol
AU - Opoku, Augustine
AU - Ballard, Carey
AU - Dafnis, Konstantinos M.
AU - Chroni, Evgenia
AU - Metaxas, Dimitris
N1 - Publisher Copyright:
© European Language Resources Association (ELRA), licensed under CC-BY-NC 4.0.
PY - 2022
Y1 - 2022
N2 - 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, linguistically annotated, video corpus of citation-form ASL signs shared by the 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 making 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.
AB - 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, linguistically annotated, video corpus of citation-form ASL signs shared by the 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 making 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.
KW - ASL
KW - ASLLRP
KW - ASLLVD
KW - WLASL
KW - gloss labels
KW - isolated sign recognition
UR - http://www.scopus.com/inward/record.url?scp=85146250976&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146250976&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85146250976
T3 - 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources, sign-lang 2022 - held in conjunction with the International Conference on Language Resources and Evaluation, LREC 2022 - Proceedings
SP - 165
EP - 172
BT - 10th Workshop on the Representation and Processing of Sign Languages
A2 - Efthimiou, Eleni
A2 - Fotinea, Stavroula-Evita
A2 - Hanke, Thomas
A2 - Hochgesang, Julie A.
A2 - Kristoffersen, Jette
A2 - Mesch, Johanna
A2 - Schulder, Marc
PB - European Language Resources Association (ELRA)
T2 - 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources, sign-lang 2022
Y2 - 20 June 2022 through 25 June 2022
ER -