@inproceedings{hassan-etal-2022-asl,
title = "{ASL}-Homework-{RGBD} Dataset: An Annotated Dataset of 45 Fluent and Non-fluent Signers Performing {A}merican {S}ign {L}anguage Homeworks",
author = "Hassan, Saad and
Seita, Matthew and
Berke, Larwan and
Tian, Yingli and
Gale, Elaine and
Lee, Sooyeon and
Huenerfauth, Matt",
editor = "Efthimiou, Eleni and
Fotinea, Stavroula-Evita and
Hanke, Thomas and
Hochgesang, Julie A. and
Kristoffersen, Jette and
Mesch, Johanna and
Schulder, Marc",
booktitle = "Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.signlang-1.11",
pages = "67--72",
abstract = "We are releasing a dataset containing videos of both fluent and non-fluent signers using American Sign Language (ASL), which were collected using a Kinect v2 sensor. This dataset was collected as a part of a project to develop and evaluate computer vision algorithms to support new technologies for automatic detection of ASL fluency attributes. A total of 45 fluent and non-fluent participants were asked to perform signing homework assignments that are similar to the assignments used in introductory or intermediate level ASL courses. The data is annotated to identify several aspects of signing including grammatical features and non-manual markers. Sign language recognition is currently very data-driven and this dataset can support the design of recognition technologies, especially technologies that can benefit ASL learners. This dataset might also be interesting to ASL education researchers who want to contrast fluent and non-fluent signing.",
}
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<abstract>We are releasing a dataset containing videos of both fluent and non-fluent signers using American Sign Language (ASL), which were collected using a Kinect v2 sensor. This dataset was collected as a part of a project to develop and evaluate computer vision algorithms to support new technologies for automatic detection of ASL fluency attributes. A total of 45 fluent and non-fluent participants were asked to perform signing homework assignments that are similar to the assignments used in introductory or intermediate level ASL courses. The data is annotated to identify several aspects of signing including grammatical features and non-manual markers. Sign language recognition is currently very data-driven and this dataset can support the design of recognition technologies, especially technologies that can benefit ASL learners. This dataset might also be interesting to ASL education researchers who want to contrast fluent and non-fluent signing.</abstract>
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%0 Conference Proceedings
%T ASL-Homework-RGBD Dataset: An Annotated Dataset of 45 Fluent and Non-fluent Signers Performing American Sign Language Homeworks
%A Hassan, Saad
%A Seita, Matthew
%A Berke, Larwan
%A Tian, Yingli
%A Gale, Elaine
%A Lee, Sooyeon
%A Huenerfauth, Matt
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y Hochgesang, Julie A.
%Y Kristoffersen, Jette
%Y Mesch, Johanna
%Y Schulder, Marc
%S Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F hassan-etal-2022-asl
%X We are releasing a dataset containing videos of both fluent and non-fluent signers using American Sign Language (ASL), which were collected using a Kinect v2 sensor. This dataset was collected as a part of a project to develop and evaluate computer vision algorithms to support new technologies for automatic detection of ASL fluency attributes. A total of 45 fluent and non-fluent participants were asked to perform signing homework assignments that are similar to the assignments used in introductory or intermediate level ASL courses. The data is annotated to identify several aspects of signing including grammatical features and non-manual markers. Sign language recognition is currently very data-driven and this dataset can support the design of recognition technologies, especially technologies that can benefit ASL learners. This dataset might also be interesting to ASL education researchers who want to contrast fluent and non-fluent signing.
%U https://aclanthology.org/2022.signlang-1.11
%P 67-72
Markdown (Informal)
[ASL-Homework-RGBD Dataset: An Annotated Dataset of 45 Fluent and Non-fluent Signers Performing American Sign Language Homeworks](https://aclanthology.org/2022.signlang-1.11) (Hassan et al., SignLang 2022)
ACL