This paper presents a systematic literature review of published studies on AI-based automated speech therapy tools for persons with speech sound disorders (SSD). The COVID-19 pandemic has initiated the requirement for automated speech therapy tools for persons with SSD making speech therapy accessible and affordable. However, there are no guidelines for designing such automated tools and their required degree of automation compared to human experts. In this systematic review, we followed the PRISMA framework to address four research questions: 1) what types of SSD do AI-based automated speech therapy tools address, 2) what is the level of autonomy achieved by such tools, 3) what are the different modes of intervention, and 4) how effective are such tools in comparison with human experts. An extensive search was conducted on digital libraries to find research papers relevant to our study from 2007 to 2022. The results show that AI-based automated speech therapy tools for persons with SSD are increasingly gaining attention among researchers. Articulation disorders were the most frequently addressed SSD based on the reviewed papers. Further, our analysis shows that most researchers proposed fully automated tools without considering the role of other stakeholders. Our review indicates that mobile-based and gamified applications were the most frequent mode of intervention. The results further show that only a few studies compared the effectiveness of such tools compared to expert Speech-Language Pathologists (SLP). Our paper presents the state-of-the-art in the field, contributes significant insights based on the research questions, and provides suggestions for future research directions.

翻译：本文对基于AI的构音障碍（SSD）自动语音治疗工具的相关研究进行了系统性文献综述。新冠疫情催生了针对构音障碍患者的自动语音治疗工具需求，使语音治疗更易获取且经济可负担。然而，目前尚无此类自动化工具的设计指南，也缺乏其与人类专家相比应达到的自动化程度标准。本综述采用PRISMA框架，围绕四个研究问题展开：1）基于AI的自动语音治疗工具针对何种类型的构音障碍；2）此类工具实现何种自主化水平；3）存在哪些不同的干预模式；4）与人类专家相比其有效性如何。我们对2007年至2022年的数字图书馆进行了广泛检索，以获取相关研究文献。结果显示，基于AI的构音障碍自动语音治疗工具正日益受到研究者关注。在综述文献中，构音障碍是最常被处理的构音障碍类型。进一步分析表明，多数研究者提出的是完全自动化工具，未充分考虑其他利益相关者的作用。综述指出，基于移动设备和游戏化的应用程序是最常见的干预模式。研究结果还显示，仅少数研究对比了此类工具与言语语言病理学家（SLP）专家的有效性差异。本文呈现了该领域的最新研究进展，基于研究问题提供了重要见解，并为未来研究方向提出了建议。