Mariana Arroyo Chavez
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Mariana Arroyo Chavez has a total of 15 co-authors. Here are the top co-authors by number of shared publications:
- Bernard Thompson
Technology Access Program, Gallaudet University, Washington DC, District of Columbia, USA
4 shared publications - Christian Vogler
Technology Access Program, Gallaudet University, Washington, District of Columbia, USA
4 shared publications - Raja Kushalnagar
Gallaudet University, Washington, District of Columbia, USA
4 shared publications - Abraham Glasser
Science, Technology, Accessibility, Mathematics, and Public Health (STAMP), Gallaudet University, Washington, District of Columbia, USA
3 shared publications - Molly Feanny
Gallaudet University, Washington, DC, USA
3 shared publications - James Waller
Gallaudet University, DC, Washington, USA
2 shared publications
Latest Publications
- Open Access
- 24 October 2026
- Published by Association for Computing Machinery (ACM)
- p. 1-22
Are Caption Metrics Broken? Latency, Deaf and Hard of Hearing User Ratings, and Cross-Technology Bias
- 24 October 2026
- Published by Association for Computing Machinery (ACM)
- p. 1-22
Open AccessAre Caption Metrics Broken? Latency, Deaf and Hard of Hearing User Ratings, and Cross-Technology Bias
- Open Access
- 28 April 2025
- Published by Association for Computing Machinery (ACM)
- p. 95-99
Barriers to Employment: The Deaf Multimedia Authoring Tax
- 28 April 2025
- Published by Association for Computing Machinery (ACM)
- p. 95-99
Open AccessBarriers to Employment: The Deaf Multimedia Authoring Tax
- Open Access
- 5 July 2024
- Published by Springer Nature
- p. 50-58
Customization of Closed Captions via Large Language Models
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.
- 5 July 2024
- Published by Springer Nature
- p. 50-58
Open AccessCustomization of Closed Captions via Large Language Models
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.
- Open Access
- 11 May 2024
- Published by Association for Computing Machinery (ACM)
- p. 1-16
How Users Experience Closed Captions on Live Television: Quality Metrics Remain a Challenge
- 11 May 2024
- Published by Association for Computing Machinery (ACM)
- p. 1-16
Open AccessHow Users Experience Closed Captions on Live Television: Quality Metrics Remain a Challenge