Work

Preferred Appearance of Captions Generated by Automatic Speech Recognition for Deaf and Hard-of-Hearing Viewers

Year: 2019

Type: conference-paper

Abstract: As the accuracy of Automatic Speech Recognition (ASR) nears human-level quality, it might become feasible as an accessibility tool for people who are Deaf and Hard of Hearing (DHH) to transcribe spoken language to text. We conducted a study using in-person laboratory methodologies, to investigate requirements and preferences for new ASR-based captioning services when used in a small group meeting context. The open-ended comments reveal an interesting dynamic between: caption readability (visibility of text) and occlusion (captions blocking the video contents). Our 105 DHH participants provided valuable feedback on a variety of caption-appearance parameters (strongly preferring familiar styles such as closed captions), and in this paper we start a discussion on how ASR captioning could be visually styled to improve text readability for DHH viewers.

Source: CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA)

DOI: 10.1145/3290607.3312921

Authors: Larwan Berke, Khaled Albusays, Matthew Seita, Matt Huenerfauth

Institution: Rochester Institute of Technology

Language: English

Topic: Subtitles and Audiovisual Media

Subfield: Language and Linguistics

Field: Arts and Humanities

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: bronze

Funders: National Science Foundation, U.S. Department of Health and Human Services