ASL-Homework-RGBD Dataset: An Annotated Dataset of 45 Fluent and Non-fluent Signers Performing American Sign Language Homeworks
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.
Saad Hassan
Assistant Professor
My research interests include human-computer interaction (HCI), accessibility, and computational social science.
Matthew Seita
Researcher at Gallaudet
Larwan Berke
Former Graduate Research Assistant at RIT
Yingli Tian
Professor at CUNY
Elaine Gale
Associate Professor at Hunter College
Sooyeon Lee
Assistant Professor at NJIT
