Increasing Video Accessibility for Visually Impaired Users with Human-in-the-Loop Machine Learning
Beste F. Yuksel, Soo Jung Kim, Seung Jung Jin, Joshua Junhee Lee, Pooyan Fazli, Umang Mathur, Vaishali Bisht, Ilmi Yoon, Yue-Ting Siu, Joshua A. Miele
Abstract
Video accessibility is crucial for blind and visually impaired individuals for education, employment, and entertainment purposes. However, professional video descriptions are costly and time-consuming. Volunteer-created video descriptions could be a promising alternative, however, they can vary in quality and can be intimidating for novice describers. We developed a Human-in-the-Loop Machine Learning (HILML) approach to video description by automating video text generation and scene segmentation while allowing humans to edit the output. Our HILML system was significantly faster and easier to use for first-time video describers compared to a human-only control condition with no machine learning assistance. The quality of the video descriptions and understanding of the topic created by the HILML system compared to the human-only condition were rated as being significantly higher by blind and visually impaired users.