ASL-Homework-RGBD Dataset: An annotated dataset of 45 fluent and non-fluent signers performing American Sign Language homeworks

TL;DR

This paper introduces a new annotated RGB-D dataset of 45 signers performing ASL homework, aimed at advancing recognition algorithms and supporting ASL learning and research.

Contribution

The dataset uniquely includes both fluent and non-fluent signers with detailed annotations, facilitating research in ASL recognition and education.

Findings

Dataset contains videos of 45 signers performing ASL homework.

Annotations include grammatical features and non-manual markers.

Supports development of recognition technologies for ASL learning.

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.