# Automated measurement of language outcomes for neurodevelopmental disorders

> **NIH NIH R01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2020 · $583,798

## Abstract

Improving conversational use of spoken language is an important goal for many new interventions and
treatments for children with neurodevelopmental disorders. However, progress in testing these treatments is
limited by the lack of informative outcome measures to indicate whether or not an intervention or treatment is
having the desired effect on a child's conversational use of language (i.e., discourse skills). The long-term
goal of the proposed renewal project is to harness the benefits of NLP to impact functional spoken language
outcomes for children with neurodevelopmental disorders. The goal of the parent R01 (R01DC012033) is to
develop and validate new Natural Language Processing (NLP) based methods that automatically measure
discourse-related skills, including language productivity (talkativeness), grammar and vocabulary, and
discourse, based on raw (i.e., not coded or annotated) transcripts of natural language samples. Our objective
in this proposal is to take the next step to evaluate the suitability of these NLP-based measures as outcomes for
children with a range of intellectual abilities, language levels, and diagnoses. NLP algorithms require choices of
pivotal parameter settings, such as word frequency dependent weights. While our previous results, involving
between-group contrasts, were insensitive to these settings, our proposed project, involving psychometric
quantities such as validity, may be sensitive to them. Building on our progress from the parent R01, we propose
to pursue three specific aims: (1) Identify pivotal parameter settings that optimize stability of NLP discourse
measures, and examine responsiveness to real change; (2) Evaluate consistency of NLP discourse measures,
and identify key measurement factors that impact consistency; and (3) Evaluate validity of NLP discourse
measures, and differences in validity as a function of diagnostic group, age, IQ, and language ability. Our
approach will focus on optimizing stability of such measures, and assessing responsiveness to change over
time, consistency across sampling contexts and different sample lengths, and validity of each measure. The
contribution of the proposed project will be to systematically assess the psychometric properties of NLP
discourse measures. The proposed research is innovative because it represents a substantial departure from
the status quo by taking the crucial next step: the development of scalable, psychometrically sound measures of
discourse skills that can be used to assess between-group differences as well as within-individual change over
time. The proposed research is significant because it is expected to result in viable spoken language outcome
measures for children with a range of neurodevelopmental disorders, making it possible to target and
meaningfully measure improvements in clinical trials and behavioral interventions. Ultimately, the successful
completion of this study will provide the immediate ability to scale up treatment evaluation...

## Key facts

- **NIH application ID:** 9895715
- **Project number:** 5R01DC012033-09
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Eric Fombonne
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $583,798
- **Award type:** 5
- **Project period:** 2011-09-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9895715

## Citation

> US National Institutes of Health, RePORTER application 9895715, Automated measurement of language outcomes for neurodevelopmental disorders (5R01DC012033-09). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9895715. Licensed CC0.

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