# Intensive Speech Motor Chaining Treatment and Artificial Intelligence Integration for Residual Speech Sound Disorders

> **NIH NIH R01** · SYRACUSE UNIVERSITY · 2024 · $501,522

## Abstract

Project Summary/Abstract
 Speech sound disorders impacting /ɹ, s, z/ may become chronic due to either ineffective or limited treat-
ment. The long-term goal is to leverage theoretical and technological advancements to accelerate the develop-
ment of accessible and effective treatments that mitigate reduced quality of life due to chronic residual speech
sound disorders (RSSD). To this end, the validated motor-based RSSD treatment Speech Motor Chaining guides
speech-language pathologists (SLPs) through high-fidelity, high-trial, rapidly adapting treatment by dosing and
manipulating several principles of motor learning in real time. SLP-led Speech Motor Chaining has been effective
for individuals whose errors persist after traditional treatment. However, at least two challenges remain: first,
optimal treatment intensity is unknown. Second, SLPs need validated avenues for evidence-based practice when
caseload size precludes optimal intensity. Therefore, the overall objective of this proposal is to optimize a suite
of theoretically motivated, high-fidelity, motor-based treatments delivered at the appropriate intensity, despite
practical barriers, for the sounds comprising 90% of RSSD: /ɹ, s, z/. The central working hypotheses, supported
by our preliminary work, are that Speech Motor Chaining is (a) more efficacious when delivered intensively (i.e.,
closely spaced for a fixed number of sessions), and (b) also beneficial when practice is led by an artificial intelli-
gence (AI) SLP. The theoretical rationale is that increasing intensity early in treatment will mitigate erred prac-
tice between sessions, improving outcomes relative to more customary practice distributions, and that reliable
AI-mediated practice is effective in the context of validated treatments. There are three aims: Aim 1: Deter-
mine how intensive/distributed treatment affects speech sound learning in RSSD. A randomized
controlled trial (n=84) will test the hypothesis that intensive SLP-led Speech Motor Chaining (i.e., bootcamp)
leads to greater gains in speech sound accuracy compared to an equivalent number of customarily distributed
sessions. Aim 2: Determine improvement in /ɹ/ production when Speech Motor Chaining practice
trials are led by an Artificial Intelligence clinician. A multiple baseline single subject design will test the
hypothesis that Chaining-AI, in which an AI SLP provides clinical feedback, facilitates clinically meaningful
change in /ɹ/ production. Aim 3: Demonstrate breadth of clinical AI capability by optimizing mis-
pronunciation classification algorithms for /s/ and /z/. Mispronunciation detection algorithms will be
trained to recognize clinical speech errors affecting /s/ and /z/, replicating expert listener judgement with clini-
cally-acceptable accuracy. This significant research addresses a critical need for theoretical/empirical guidance
for treatment intensity, offering sorely needed recommendations in a system where ~6 million American adults
have unres...

## Key facts

- **NIH application ID:** 10796960
- **Project number:** 5R01DC020959-02
- **Recipient organization:** SYRACUSE UNIVERSITY
- **Principal Investigator:** Jonathan Preston
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $501,522
- **Award type:** 5
- **Project period:** 2023-03-01 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10796960

## Citation

> US National Institutes of Health, RePORTER application 10796960, Intensive Speech Motor Chaining Treatment and Artificial Intelligence Integration for Residual Speech Sound Disorders (5R01DC020959-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10796960. Licensed CC0.

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