Abstract：
BACKGROUND: The emergence of the coronavirus disease 2019 (COVID-19) pandemic has resulted in communication being heightened as one of the critical aspects in the implementation of interventions. Delays in the relaying of vital information by policymakers have the potential to be detrimental, especially for the hearing impaired.
OBJECTIVE: This study aims to conduct a scoping review on the application of artificial intelligence (AI) for real-time speech-to-text to sign language translation and consequently propose an AI-based real-time translation solution for South African languages from speech-to-text to sign language.
METHODS: Electronic bibliographic databases including ScienceDirect, PubMed, Scopus, MEDLINE and ProQuest were searched to identify peer-reviewed publications published in English between 2019 and 2021 that provided evidence on AI-based real-time speech-to-text to sign language translation as a solution for the hearing impaired. This review was done as a precursor to the proposed real-time South African translator.
RESULTS: The review revealed a dearth of evidence on the adoption and/or maximisation of AI and machine learning (ML) as possible solutions for the hearing impaired. There is a clear lag in clinical utilisation and investigation of these technological advances, particularly in the African continent.
CONCLUSIONS: Assistive technology that caters specifically for the South African community is essential to ensuring a two-way communication between individuals who can hear clearly and individuals with hearing impairments, thus the proposed solution presented in this article.
OBJECTIVE: This study aims to conduct a scoping review on the application of artificial intelligence (AI) for real-time speech-to-text to sign language translation and consequently propose an AI-based real-time translation solution for South African languages from speech-to-text to sign language.
METHODS: Electronic bibliographic databases including ScienceDirect, PubMed, Scopus, MEDLINE and ProQuest were searched to identify peer-reviewed publications published in English between 2019 and 2021 that provided evidence on AI-based real-time speech-to-text to sign language translation as a solution for the hearing impaired. This review was done as a precursor to the proposed real-time South African translator.
RESULTS: The review revealed a dearth of evidence on the adoption and/or maximisation of AI and machine learning (ML) as possible solutions for the hearing impaired. There is a clear lag in clinical utilisation and investigation of these technological advances, particularly in the African continent.
CONCLUSIONS: Assistive technology that caters specifically for the South African community is essential to ensuring a two-way communication between individuals who can hear clearly and individuals with hearing impairments, thus the proposed solution presented in this article.
摘要：
背景：2019年冠状病毒病（COVID-19）大流行的出现导致沟通成为实施干预措施的关键方面之一。决策者传递重要信息的延误有可能是有害的，特别是对于听力受损的人。
目的：本研究旨在对人工智能（AI）在实时语音到文本到手语翻译中的应用进行范围审查，并因此提出基于AI的实时翻译解决方案南非语言从语音到文本到手语。
方法：电子书目数据库，包括ScienceDirect，PubMed,Scopus,搜索了MEDLINE和ProQuest，以识别2019年至2021年间以英语出版的同行评审出版物，这些出版物提供了基于AI的实时语音到文本到手语翻译的证据，作为听力受损者的解决方案。这项审查是拟议的南非实时翻译的先驱。
结果：评论显示，缺乏采用和/或最大化AI和机器学习（ML）作为听力受损者可能解决方案的证据。这些技术进步的临床应用和研究明显滞后，尤其是在非洲大陆。
结论：专门为南非社区服务的辅助技术对于确保听得清楚的人与有听力障碍的人之间的双向交流至关重要，因此本文提出的解决方案。
目的：本研究旨在对人工智能（AI）在实时语音到文本到手语翻译中的应用进行范围审查，并因此提出基于AI的实时翻译解决方案南非语言从语音到文本到手语。
方法：电子书目数据库，包括ScienceDirect，PubMed,Scopus,搜索了MEDLINE和ProQuest，以识别2019年至2021年间以英语出版的同行评审出版物，这些出版物提供了基于AI的实时语音到文本到手语翻译的证据，作为听力受损者的解决方案。这项审查是拟议的南非实时翻译的先驱。
结果：评论显示，缺乏采用和/或最大化AI和机器学习（ML）作为听力受损者可能解决方案的证据。这些技术进步的临床应用和研究明显滞后，尤其是在非洲大陆。
结论：专门为南非社区服务的辅助技术对于确保听得清楚的人与有听力障碍的人之间的双向交流至关重要，因此本文提出的解决方案。
