MORA: AI-Mediated Story-Based practice for Speech Sound Disorder from Clinic to Home
Sumin Hong, Xavier Briggs, Qingxiao Zheng, Yao Du, Jinjun Xiong, Toby Jia-jun Li

TL;DR
MORA is an interactive, story-based system that enhances speech sound disorder therapy by integrating AI, engaging narratives, and clinician configurability to improve home practice and generalization.
Contribution
It introduces a novel AI-mediated platform that embeds speech practice into dynamic stories, enabling active participation, clinician customization, and bridging clinic-home therapy gaps.
Findings
Strong alignment with established treatments
Enhanced engagement and literacy potential
Supports clinician configurability and feedback
Abstract
Speech sound disorder is among the most common communication challenges in preschool children. Home-based practice is essential for effective therapy and for acquiring generalization of target sounds, yet sustaining engaging and consistent practice remains difficult. Existing story-based activities, despite their potential for sound generalization and educational benefits, are often underutilized due to limited interactivity. Moreover, many practice tools fail to sufficiently integrate speech-language pathologists into the process, resulting in weak alignment with clinical treatment plans. To address these limitations, we present MORA, an interactive story-based practice system. MORA introduces three key innovations. First, it embeds target sounds and vocabulary into dynamic, character-driven conversational narratives, requiring children to actively produce speech to progress the story,…
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