Towards Human-Centered AI in Speech Therapy: Perspectives from a Low-Resource Setting

Background: While Speech-Language Pathologists (SLPs) are crucial in addressing Speech Sound Disorder (SSD), a global shortage of SLPs poses significant challenges in providing speech therapy services, particularly in impoverished and rural areas. Despite the potential of AI-based automated speech therapy tools, concerns such as job displacement, algorithmic bias, and privacy issues persist. Purpose: This study adopted a Human-Centered AI (HCAI) approach to understand the needs and perspectives of SLPs, aiming to inform the development of a Human-Centered AI-based Speech Therapy Tool (HCAI-STT) for children with SSD. Methods: A qualitative study using deductive reflexive thematic analysis with MAXQDA software was conducted to explore the needs and perspectives of SLPs. Results: The domain understanding theme highlighted the complexity of functional SSD, emphasizing unknown etiology, parental concerns, and the developmental nature of speech acquisition. Current practices involve using digital tools under supervision and adhering to therapy guidelines. Key challenges included accessibility issues, socio-economic constraints, and the absence of a standardized Assamese Photo Articulation Test (PAT). Future directions highlighted the need for technology-based interventions, culturally relevant audio-visual stimuli, mobile-based solutions, and affordable tools. Conclusion: The findings emphasize the necessity for a culturally tailored, technologically advanced approach to speech therapy. Recommendations include integrating Assamese PAT, culturally relevant audio-visual stimuli, AI-based diagnostic and feedback tools, and home-based therapy with supervision. These insights will guide the development of the HCAI-STT, enhancing AI integration in speech therapy and improving quality and accessibility. Future research will engage additional stakeholders and develop and evaluate the tool’s usability, efficacy, and effectiveness.