@misc{RN11,
   author = {Almeida, Rafael and Duarte, Carlos},
   title = {Analysis of automated contrast checking tools},
   publisher = {Association for Computing Machinery},
   pages = {Article 18},
   keywords = {accessibility, automated evaluation, color contrast, text},
   DOI = {10.1145/3371300.3383348},
   url = {https://doi.org/10.1145/3371300.3383348},
   year = {2020},
   type = {Conference Paper}
}

@article{RN2,
   author = {Ismailova, Rita and Inal, Yavuz},
   title = {Comparison of Online Accessibility Evaluation Tools: An Analysis of Tool Effectiveness},
   journal = {IEEE Access},
   volume = {10},
   pages = {58233-58239},
   ISSN = {2169-3536},
   DOI = {10.1109/access.2022.3179375},
   url = {https://dx.doi.org/10.1109/access.2022.3179375},
   year = {2022},
   type = {Journal Article}
}

@article{RN3,
   author = {Leotta, Maurizio and Mori, Fabrizio and Ribaudo, Marina},
   title = {Evaluating the effectiveness of automatic image captioning for web accessibility},
   journal = {Universal Access in the Information Society},
   volume = {22},
   number = {4},
   pages = {1293-1313},
   ISSN = {1615-5289},
   DOI = {10.1007/s10209-022-00906-7},
   url = {https://dx.doi.org/10.1007/s10209-022-00906-7},
   year = {2023},
   type = {Journal Article}
}

@article{RN8,
   author = {Lv, Zhihan},
   title = {Generative artificial intelligence in the metaverse era},
   journal = {Cognitive Robotics},
   volume = {3},
   pages = {208-217},
   abstract = {Generative artificial intelligence (AI) is a form of AI that can autonomously generate new content, such as text, images, audio, and video. Generative AI provides innovative approaches for content production in the metaverse, filling gaps in the development of the metaverse. Products such as ChatGPT have the potential to enhance the search experience, reshape information generation and presentation methods, and become new entry points for online traffic. This is expected to significantly impact traditional search engine products, accelerating industry innovation and upgrading. This paper presents an overview of the technologies and prospective applications of generative AI in the breakthrough of metaverse technology and offers insights for increasing the effectiveness of generative AI in creating creative content.},
   ISSN = {2667-2413},
   DOI = {https://doi.org/10.1016/j.cogr.2023.06.001},
   url = {https://www.sciencedirect.com/science/article/pii/S2667241323000198},
   year = {2023},
   type = {Journal Article}
}

@article{RN5,
   author = {Millett, Pam},
   title = {Accuracy of Speech-to-Text Captioning for Students Who are Deaf or Hard of Hearing},
   journal = {Journal of Educational, Pediatric & (Re)Habilitative Audiology},
   volume = {25},
   pages = {1-13},
   note = {research; tables/charts. Journal Subset: Allied Health. Grant Information: This research study was supported by Minor Research Grant from the Faculty of Education at York University, Toronto, Ontario, Canada.},
   abstract = {Speech-to-text technology (also referred to as automatic speech recognition, or ASR) is now available in apps and software, offering opportunities for deaf/hard of hearing students to have real time captioning at their fingertips. However, speech-to-text technology must be proven to be accurate before it should be considered as an accommodation for students. This study assessed the accuracy of eight apps, software and platforms to provide captions for i) a university lecture given by a native English speaker in real time ii) a video of the lecture, and iii) a conversation between 3 students in real time, using real speech under controlled acoustical conditions. Accuracy of transcribed speech was measured in two ways: a Total Accuracy score indicating % of words transcribed accurately, and as a Meaning Accuracy score, which considered transcription errors which impacted the meaning of the message. Technologies evaluated included Interact Streamer, Ava, Otter, Google Slides, Microsoft Stream, Microsoft Translator, Camtasia Studio and YouTube. For the lecture condition, 4 of 5 technologies evaluated exceeded 90% accuracy, with Google Slides and Otter achieving 98 and 99%% accuracy. Overall accuracy for video captioning was highest, with 5 of 6 technologies achieving greater than 90% accuracy, and accuracy rates for YouTube, Microsoft Stream and Otter of 98-99%. Accuracy for captioning a real time conversation between 3 students was greater than 90% for both technologies evaluated, Ava and Microsoft Translator. Results suggest that, given excellent audio quality, speech-to-text technology accuracy is sufficient to consider use by postsecondary students.},
   keywords = {Deafness
Hearing Disorders
Voice Recognition Systems
Software
Human
Descriptive Statistics
Students
Funding Source},
   ISSN = {2378-0916},
   url = {https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib&db=ccm&AN=155359972&site=ehost-live&custid=s3358796},
   year = {2021},
   type = {Journal Article}
}

@article{RN9,
   author = {Morris, Amanda},
   title = {For Blind Internet Users, the Fix Can Be Worse Than the Flaws},
   journal = {The New York times},
   keywords = {Actions and defenses
Automation
Blindness
Employees
Internet
Jurisprudence
Litigation
Software
Vision disorders},
   ISSN = {1553-8095},
   year = {2022},
   type = {Journal Article}
}

@misc{RN6,
   author = {Vigo, Markel and Brown, Justin and Conway, Vivienne},
   title = {Benchmarking web accessibility evaluation tools: measuring the harm of sole reliance on automated tests},
   publisher = {Association for Computing Machinery},
   pages = {Article 1},
   keywords = {WCAG, accessibility, benchmark, evaluation, testing, tools},
   DOI = {10.1145/2461121.2461124},
   url = {https://doi.org/10.1145/2461121.2461124},
   year = {2013},
   type = {Conference Paper}
}

@article{RN7,
   author = {Xu, Yongjun and Liu, Xin and Cao, Xin and Huang, Changping and Liu, Enke and Qian, Sen and Liu, Xingchen and Wu, Yanjun and Dong, Fengliang and Qiu, Cheng-Wei and Qiu, Junjun and Hua, Keqin and Su, Wentao and Wu, Jian and Xu, Huiyu and Han, Yong and Fu, Chenguang and Yin, Zhigang and Liu, Miao and Roepman, Ronald and Dietmann, Sabine and Virta, Marko and Kengara, Fredrick and Zhang, Ze and Zhang, Lifu and Zhao, Taolan and Dai, Ji and Yang, Jialiang and Lan, Liang and Luo, Ming and Liu, Zhaofeng and An, Tao and Zhang, Bin and He, Xiao and Cong, Shan and Liu, Xiaohong and Zhang, Wei and Lewis, James P. and Tiedje, James M. and Wang, Qi and An, Zhulin and Wang, Fei and Zhang, Libo and Huang, Tao and Lu, Chuan and Cai, Zhipeng and Wang, Fang and Zhang, Jiabao},
   title = {Artificial intelligence: A powerful paradigm for scientific research},
   journal = {The Innovation},
   volume = {2},
   number = {4},
   pages = {100179},
   ISSN = {2666-6758},
   DOI = {10.1016/j.xinn.2021.100179},
   url = {https://dx.doi.org/10.1016/j.xinn.2021.100179},
   year = {2021},
   type = {Journal Article}
}