Extension of a User Model for Promoting the Development of Applications to Support Auditory Rehabilitation

Céspedes-Hernández, David; Rodríguez-Vizzuett, Liliana; González-Calleros, Juan Manuel; Muñoz-Arteaga, Jaime

doi:10.1007/978-3-319-55666-6_3

Cited by 2 publications

(1 citation statement)

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“…Furthermore, Sztaho et al proposed a fully automated speech therapy tool by dis- playing visual feedback on intensity(accent), intonation, and rhythm to children with hearing impairments (Sztahó et al, 2018). In another similar study, Hernandez et al developed a serious game with an automatic feedback feature for hearing impaired children (Céspedes-Hernández et al, 2015). Ballard et al performed a feasibility study of their tablet-based, fully automated therapy tool for children with apraxia without any role of SLP and other stakeholders (Ballard et al, 2019).…”

Section: Level Of Autonomy (Rq2)mentioning

confidence: 99%

AI-Based Automated Speech Therapy Tools for persons with Speech Sound Disorders: A Systematic Literature Review

Deka,

Shrivastava,

Abraham

et al. 2024

Preprint

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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 the conventional speech therapy given by Speech Language Pathologists (SLPs). 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 the conventional mode of speech therapy. 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 the conventional mode of speech therapy. 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.

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Section: Level Of Autonomy (Rq2)mentioning

confidence: 99%