# Quantifying articulatory performance in children with dysarthria:  Development of an automated metric for clinical use

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $604,276

## Abstract

Project Summary
Half of children with cerebral palsy (CP) have dysarthria, which has well documented negative effects on
speech intelligibility. A primary aim of treatment is improving speech intelligibility. Current pediatric dysarthria
interventions focus on clear and/or loud speech, phonation, speech rate, or some combination of these. While
several interventions have resulted in intelligibility gains, there is substantial variability in outcomes. Additional
interventions are needed to ensure that intelligibility can be maximized for all children. Laboratory studies have
consistently identified the articulatory system as the largest contributor to intelligibility deficits in dysarthria,
implicating variables such as vowel space and F2 slope. However, these measures are not clinically accessible,
nor do they translate to specific treatment targets. Given the primacy of the articulatory system to intelligibility
and developmental malleability associated with the acquisition of speech in children, there is a need for
sensitive metrics to quantify articulation using clinically meaningful units (i.e., speech sounds). Such metrics
would enable us to understand how different speech sounds contribute to intelligibility and would lead to
advancements in the development of data-driven interventions for improving intelligibility in children with
dysarthria, complementing existing therapies. Our goal is to develop a clinical tool that yields objective,
continuous, and automatic quantification of speech sound articulation from connected speech in children; we
will use this tool to quantify the contributions of individual phonemes to intelligibility. To do this, we will use
state-of-the art speech analytics involving machine learning for acoustic modeling, and clinical research in
speech pathology to refine and validate algorithms that specify a phoneme log-likelihood ratio (PLLR) for each
phoneme in English. We will use the PLLR to create growth curves for the development of phoneme articulation
based on data from 750 typically developing children between the ages of 2 ½ and 10 years, and to
characterize the contribution of individual phonemes to intelligibility by age in these children. We will then
examine 700 longitudinal speech samples from children with dysarthria between the ages of 2 ½ and 10 years,
and identify how they differ from typical children in phoneme development and how speech sound articulation
contributes to intelligibility. The outcome of this research is an algorithm that can quantify phoneme articulation
in children, indicating a child’s performance relative to norms for each phoneme. Results will specify the relative
contribution of individual phonemes to intelligibility, and how this changes developmentally and in the context of
dysarthria. A fine-grained understanding of the impact of dysarthria on phoneme development and subsequent
contributions to intelligibility has never before been feasible and will have direct clinical and th...

## Key facts

- **NIH application ID:** 10832613
- **Project number:** 5R01DC019645-03
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Visar Berisha
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $604,276
- **Award type:** 5
- **Project period:** 2022-05-01 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10832613

## Citation

> US National Institutes of Health, RePORTER application 10832613, Quantifying articulatory performance in children with dysarthria:  Development of an automated metric for clinical use (5R01DC019645-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10832613. Licensed CC0.

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