ASL-Homework-RGBD Dataset: An annotated dataset of 45 fluent and non-fluent signers performing American Sign Language homeworks
This dataset addresses the need for annotated data to develop technologies for ASL learners and education researchers, though it is incremental as it builds on existing data-driven approaches.
The authors introduced the ASL-Homework-RGBD dataset, containing videos of 45 fluent and non-fluent signers performing American Sign Language homework assignments, annotated for grammatical features and non-manual markers, to support computer vision algorithms for detecting ASL fluency attributes.
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.