Richard E. Ladner

Aashaka Desai, Lauren Berger, Fyodor O. Minakov et al. · uw

Sign languages are used as a primary language by approximately 70 million D/deaf people world-wide. However, most communication technologies operate in spoken and written languages, creating inequities in access. To help tackle this problem, we release ASL Citizen, the first crowdsourced Isolated Sign Language Recognition (ISLR) dataset, collected with consent and containing 83,399 videos for 2,731 distinct signs filmed by 52 signers in a variety of environments. We propose that this dataset be used for sign language dictionary retrieval for American Sign Language (ASL), where a user demonstrates a sign to their webcam to retrieve matching signs from a dictionary. We show that training supervised machine learning classifiers with our dataset advances the state-of-the-art on metrics relevant for dictionary retrieval, achieving 63% accuracy and a recall-at-10 of 91%, evaluated entirely on videos of users who are not present in the training or validation sets. An accessible PDF of this article is available at the following link: https://aashakadesai.github.io/research/ASLCitizen_arxiv_updated.pdf

Shaun Kane, Richard Ladner, Clayton Lewis

Access to content and services online is increasingly important for everyone, including people with disabilities. National commitments, including the Americans with Disabilities Act, and international resolutions, including the United Nations Declaration of the Rights of Persons with Disabilities, call for work to ensure that people with disabilities can participate fully in the online world. Gains in education, employment and health, as well as in civic engagement, social participation, and personal independence will follow from enhanced inclusion online. Research in many areas of computer science, including recognition technology, natural language processing, personalization, software architecture, and others, is needed to secure these benefits. Organizing this research calls for partnerships among academic researchers, federal agencies, and commercial organizations, as well as effective division of labor and cooperation between computer scientists, behavioral scientists, advocacy groups, and consumers.