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<p>While Speech-Language Pathologists (SLPs) are the primary professionals responsible for the diagnosis and treatment of communication and swallowing disorders, including 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. This study aimed to understand the needs and perspectives of SLPs regarding speech therapy for children with SSD, in order to inform the design of a Human-Centered AI-based Speech Therapy Tool (HCAI-STT). A qualitative study was conducted using semi-structured interviews comprising open-ended questions with seven SLPs, recruited through purposive sampling. Thematic analysis was applied using MAXQDA software to analyse the transcribed interview data. The domain understanding theme captured the SLPs’ domain knowledge and experiences with functional SSD, highlighting its complexity, 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. 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.</p>

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Towards human-centered AI in speech therapy: perspectives from a low-resource setting

  • 期刊论文

  • 发表日期：

    2025年12月31日

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  • Chinmoy Deka,
  • Abhishek Shrivastava,
  • Rishav Kumar

摘要

While Speech-Language Pathologists (SLPs) are the primary professionals responsible for the diagnosis and treatment of communication and swallowing disorders, including 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. This study aimed to understand the needs and perspectives of SLPs regarding speech therapy for children with SSD, in order to inform the design of a Human-Centered AI-based Speech Therapy Tool (HCAI-STT). A qualitative study was conducted using semi-structured interviews comprising open-ended questions with seven SLPs, recruited through purposive sampling. Thematic analysis was applied using MAXQDA software to analyse the transcribed interview data. The domain understanding theme captured the SLPs’ domain knowledge and experiences with functional SSD, highlighting its complexity, 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. 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.