AI-based automated speech therapy tools for persons with speech sound disorder: a systematic literature review

Abstract

ABSTRACT In response to the limited availability of speech therapy services, researchers are developing AI-based automated speech therapy tools for individuals with speech sound disorder (SSD). However, their effectiveness/efficacy compared to conventional speech therapy remains unclear, and no guidelines exist for designing these tools or their required automation levels compared to therapy by speech-language pathologists (SLPs). Moreover, AI applications raise concerns about job displacement, biased algorithms, and privacy issues. This systematic review aims to provide comprehensive insights into AI-based automated speech therapy, focusing on (i) types of SSD addressed; (ii) AI techniques used; (iii) autonomy levels achieved; (iv) delivery modes; and (v) effectiveness/efficacy of these tools. PRISMA guidelines were applied across five databases for studies published between January 2007 and February 2022. Twenty-four studies that met the inclusion and exclusion criteria were included. Results suggest that articulation disorders are the most frequently addressed SSD. Various AI techniques were applied, ranging from traditional automatic speech recognition (ASR) to advanced methods. Most studies proposed fully automated tools, often overlooking the role of other stakeholders. Computer-based and gamified applications were the most common intervention modes. The results suggest the potential of AI-based automated speech therapy tools for individuals with SSD; however, only a few studies have compared their effectiveness/efficacy with conventional speech therapy. Further research is needed to develop speech corpora for under-represented languages, apply a Human-Centered AI approach, conduct usability studies on intervention modes, and perform more rigorous effectiveness studies.