{"title":"媒体无障碍自动化:通过生成式人工智能算法分析音频描述的方法","authors":"Daniel Bergin, Brett Oppegaard","doi":"10.1080/10572252.2024.2372771","DOIUrl":null,"url":null,"abstract":"A surge in public availability of emerging GenAI-AD has brought back the promises of automated accessibility for people who cannot see or see well. This article tests those promises through a double-rendering method that asks GenAI-AD engines to describe a simple portrait of a person and then returns these generated texts into GenAI-AD engines for visualizations of what they earlier had described, revealing insights about GenAI efficacies","PeriodicalId":45536,"journal":{"name":"Technical Communication Quarterly","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automating Media Accessibility: An Approach for Analyzing Audio Description Across Generative Artificial Intelligence Algorithms\",\"authors\":\"Daniel Bergin, Brett Oppegaard\",\"doi\":\"10.1080/10572252.2024.2372771\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A surge in public availability of emerging GenAI-AD has brought back the promises of automated accessibility for people who cannot see or see well. This article tests those promises through a double-rendering method that asks GenAI-AD engines to describe a simple portrait of a person and then returns these generated texts into GenAI-AD engines for visualizations of what they earlier had described, revealing insights about GenAI efficacies\",\"PeriodicalId\":45536,\"journal\":{\"name\":\"Technical Communication Quarterly\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Technical Communication Quarterly\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/10572252.2024.2372771\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMMUNICATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technical Communication Quarterly","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10572252.2024.2372771","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMMUNICATION","Score":null,"Total":0}
Automating Media Accessibility: An Approach for Analyzing Audio Description Across Generative Artificial Intelligence Algorithms
A surge in public availability of emerging GenAI-AD has brought back the promises of automated accessibility for people who cannot see or see well. This article tests those promises through a double-rendering method that asks GenAI-AD engines to describe a simple portrait of a person and then returns these generated texts into GenAI-AD engines for visualizations of what they earlier had described, revealing insights about GenAI efficacies