Intensive motor-based speech therapy with AI support for lingering r, s, and z sounds
Official title: Intensive Speech Motor Chaining Treatment and Artificial Intelligence Integration for Residual Speech Sound Disorders
Funded at Syracuse University · NIH-11287892 · Data updated 2026-11-12
This project explores whether lots of high-quality practice using a motor-based method and an AI-guided program can help children and adults with persistent r, s, and z sound errors.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Syracuse University NIH-funded |
| Lab location | 1 site (Syracuse, United States) |
| Project start | March 1, 2023 |
| Project end | February 29, 2028 |
| Funding year | 2026 |
| Award (this year) | $465,944 |
| NIH project number | 5R01DC020959-04 |
| Project ID | NIH-11287892 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are children and adults with chronic or residual speech sound disorders specifically affecting the r, s, or z sounds who have not fully improved with traditional therapy.
✕Less likely to help
People whose speech differences stem primarily from structural issues (for example untreated cleft palate), rapidly progressive neurological disease, or who cannot follow instructions or attend frequent sessions may not benefit.
What this research studies
If you join, you'll receive a motor-focused approach called Speech Motor Chaining that emphasizes many quick, high-quality practice trials for the problem sounds /r/, /s/, and /z/. The team will deliver tightly spaced, high-intensity sessions and compare traditional therapist-led practice with practice guided by an AI speech-language tool to see if the AI can provide effective, accessible practice when therapist time is limited. The AI is designed to give real-time feedback and adapt practice to your performance, while clinicians fine-tune treatment principles. The overall aim is to find ways to reach the right amount of practice for better outcomes even when clinic resources are constrained.
Why it matters
Potential benefit: If successful, this could speed up correction of persistent r, s, and z errors and expand access to effective practice through AI-supported programs.
How similar studies have gone: Motor-based Speech Motor Chaining has shown promise in prior work for residual sound errors, while AI-led speech practice is a newer approach with more limited clinical evidence so far.
Where this research is happening
-
Syracuse University
Syracuse, United States Active
Researchers
- Principal investigator: Preston, Jonathan — Syracuse University
- Study coordinator: Preston, Jonathan
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.