Project Summary/Abstract
Speech sound disorders impacting /¿, 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: /¿, 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 /¿/ 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 /¿/ 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 unresolved RSSD. This innovative research accelerates a paradigm shift in which combined SLP/AI service
delivery could overcome barriers to effective, accessible, and sufficiently intensive treatment, for 90% of RSSD.
