# Biofeedback-Enhanced Treatment for Speech Sound Disorder: Randomized Controlled Trial and Delineation of Sensorimotor Subtypes

> **NIH NIH R01** · NEW YORK UNIVERSITY · 2021 · $268,779

## Abstract

Project Summary/Abstract
Children with speech sound disorder show diminished accuracy and intelligibility in spoken communication and
may thus be perceived as less capable or intelligent than peers, with negative consequences for both socio-
emotional and socioeconomic outcomes [1]–[3]. While most speech errors resolve by the late school-age
years, between 2-5% of speakers exhibit residual speech sound disorder (RSSD) that persists through adoles-
cence or even adulthood [4], [5], reflecting about 6 million cases in the US. In a series of experimental studies
since 2013, our research team has demonstrated that treatment incorporating technologically-enhanced feed-
back can improve speech production in individuals with RSSD who have not responded to previous interven-
tion [6]–[10]. The primary objective of the parent award (R01 DC017476, “Biofeedback-Enhanced Treatment
for Speech Sound Disorder”) is to conduct the first well-powered randomized controlled trial comparing tradi-
tional vs biofeedback intervention for the most common type of RSSD, misarticulation of the English /r/ sound.
 Treatment of RSSD could also be enhanced through the development of tools incorporating artificial
intelligence/machine learning (AI/ML). Applications with automated scoring of speech sounds could in principle
be used to augment clinician services and achieve higher-intensity practice for faster progress. However, no
computerized treatment to date has demonstrated sufficient accuracy for clinical use with children [11]. Existing
systems are limited primarily by the fact that publicly available speech corpora have very little representation of
either children or individuals with speech impairments. This data scarcity represents a fundamental issue hin-
dering advances in clinical applications of automatic speech recognition (ASR) [12]. The proposed sup-
plement will address this barrier by modifying and augmenting PERCEPT (Perceptual Error Rating for the Clin-
ical Evaluation of Phonetic Targets), an existing corpus of acoustic recordings of child speech assembled
through the parent award and the investigators’ previous NIH-funded research since 2013. AI/ML applications
of the augmented database are expected to have a twofold scientific impact. First, we anticipate direct benefits
for children with RSSD affecting /r/, whose speech samples make up the majority of the current corpus. We will
use our database to train a neural network to classify novel child productions containing /r/ as correct or incor-
rect. This classifier could then be incorporated into AI tools to increase the efficacy of intervention for children
with RSSD. Second, we anticipate that engineers working on the broader problem of ASR for child or clinical
speech will be interested in using PERCEPT for model training, especially after the corpus is augmented with
more diverse data, as proposed here. We expect to show that acoustic models generated with PERCEPT can
improve the performance of current...

## Key facts

- **NIH application ID:** 10412492
- **Project number:** 3R01DC017476-03S2
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Tara McAllister
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $268,779
- **Award type:** 3
- **Project period:** 2019-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10412492

## Citation

> US National Institutes of Health, RePORTER application 10412492, Biofeedback-Enhanced Treatment for Speech Sound Disorder: Randomized Controlled Trial and Delineation of Sensorimotor Subtypes (3R01DC017476-03S2). Retrieved via MORVS 2026-10-25 from https://api.morvs.ai/grant/nih/10412492. Licensed CC0.

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