This paper presents a mixed methods study on how deaf, hard of hearing and hearing viewers perceive live TV caption quality with captioned video stimuli designed to mirror TV captioning experiences. To assess caption quality, we used four commonly-used quality metrics focusing on accuracy: word error rate, weighted word error rate, automated caption evaluation (ACE), and its successor ACE2. We calculated the correlation between the four quality metrics and viewer ratings for subjective quality and found that the correlation was weak, revealing that other factors besides accuracy affect user ratings. Additionally, even high-quality captions are perceived to have problems, despite controlling for confounding factors. Qualitative analysis of viewer comments revealed three major factors affecting their experience: Errors within captions, difficulty in following captions, and caption appearance. The findings raise questions as to how objective caption quality metrics can be reconciled with the user experience across a diverse spectrum of viewers.

Abstract This study investigates the feasibility of employing artificial intelligence and large language models (LLMs) to customize closed captions/subtitles to match the personal needs of deaf and hard of hearing viewers. Drawing on recorded live TV samples, it compares user ratings of caption quality, speed, and understandability across five experimental conditions: unaltered verbatim captions, slowed-down verbatim captions, moderately and heavily edited captions via ChatGPT, and lightly edited captions by an LLM optimized for TV content by AppTek, LLC. Results across 16 deaf and hard of hearing participants show a significant preference for verbatim captions, both at original speeds and in the slowed-down version, over those edited by ChatGPT. However, a small number of participants also rated AI-edited captions as best. Despite the overall poor showing of AI, the results suggest that LLM-driven customization of captions on a per-user and per-video basis remains an important avenue for future research.