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Bernard Thompson

Publications and source records attributed to Bernard Thompson.

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Are Caption Metrics Broken? Latency, Deaf and Hard of Hearing User Ratings, and Bias across Technologies

Live captions on TV often contain errors and timing issues, making it hard for deaf and hard-of-hearing (DHH) viewers to follow dialog. It is essential that caption quality metrics reflect the lived DHH TV viewing experience. To this end, we describe a U.S.-based large-scale online survey with 216 validated participants, who provided 302 responses containing a cumulative 4,832 data points. Participants viewed videos drawn from a pool of 70 clips recorded from live TV, and were asked to rate the caption quality and subjective understanding of the content across four conditions: TV captions as originally recorded with up to 7-12 seconds delay, TV captions synchronized with audio, Automatic Speech Recognition (ASR)-generated captions synchronized with audio, and ASR captions with an average two-second delay. All captions were evaluated against the Word Error Rate (WER), Automated Caption Evaluation (ACE2) and Number, Edition and Recognition (NER) metrics. Results show that TV and ASR captions were rated similarly. For TV captions, all three metrics were moderately-to-highly correlated with viewer ratings, but far less so for ASR captions, making them far from technology-neutral. Additionally, caption latencies significantly impact the viewer experience, especially typical 7-12-second TV delays. We discuss the implications for the adoption of caption quality metrics.

cs.HC

How Users Experience Closed Captions on Live Television: Quality Metrics Remain a Challenge

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.

cs.HC