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| Main Authors: | , , , , |
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| Format: | Preprint |
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2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2204.10325 |
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| _version_ | 1866916212845314048 |
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| author | Deka, Chinmoy Shrivastava, Abhishek Abraham, Ajish K. Nautiyal, Saurabh Chauhan, Praveen |
| author_facet | Deka, Chinmoy Shrivastava, Abhishek Abraham, Ajish K. Nautiyal, Saurabh Chauhan, Praveen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_10325 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | AI-Based Automated Speech Therapy Tools for persons with Speech Sound Disorders: A Systematic Literature Review Deka, Chinmoy Shrivastava, Abhishek Abraham, Ajish K. Nautiyal, Saurabh Chauhan, Praveen Human-Computer Interaction Artificial Intelligence 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. |
| title | AI-Based Automated Speech Therapy Tools for persons with Speech Sound Disorders: A Systematic Literature Review |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2204.10325 |