# Infant Vocalizations as Early Markers of Autism Spectrum Disorder

> **NIH NIH R03** · CHILDREN'S HOSP OF PHILADELPHIA · 2020 · $88,000

## Abstract

Abstract
The goal of this R03 is to characterize longitudinal vocal development in infants and toddlers at risk for ASD and
use these metrics to predict subsequent diagnosis and dimensional social/language abilities. There is a critical
need for reliable markers of autism spectrum disorder (ASD) that can be used to detect the condition in infancy
and hasten the onset of early intervention services. Preliminary data from our team indicates that infant
vocalization features distinguish groups beginning in the first year of life, and account for significant variance in
later diagnostic status and social/language phenotype. For example, we found that infants later diagnosed with
ASD produce fewer speech-like vocalizations, fewer vocalizations directed toward others, more crying, and
altered vocalization acoustics. However, the longitudinal development of these early-emerging differences has
not been completely described, and while they hold promise as early markers, their ability to predict individual
clinical outcomes has not been directly tested in a large sample. Therefore, the power of these early vocalization
differences to improve clinical detection has not been fully harnessed.
To address this gap, we leverage a large existing dataset collected through the Infant Brain Imaging Study (IBIS;
NICHD R01HD055741, PI: Dr. Joseph Piven). In this successful longitudinal multi-site study, infants at high and
low familial risk for ASD completed video-recorded behavioral assessments at 6, 12, and 24 months of age, as
well as neuroimaging at the same time points. Our research plan includes annotating infant vocalizations
produced during these interactions using an expanded version of a coding scheme we have already developed,
manualized, and trained annotators to reliably implement, and measuring the acoustic properties of each
annotated vocalization. In Aim 1, we will use mixed models to analyze developmental group differences in
vocalization features, determining which qualities distinguish groups and when. In Aim 2, we will use machine
learning to test whether vocalization features produced during the first year of life can accurately predict
diagnostic and social/language outcomes at age 2. This work will provide evidence of the clinical utility of infant
vocalizations as pre-diagnostic behavioral biomarkers, setting the stage for subsequent studies of the
relationship between early vocal behavior and brain development (measured using previously collected
neuroimaging data from the same participants).
The

## Key facts

- **NIH application ID:** 9894787
- **Project number:** 5R03DC017944-02
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** Julia Parish-Morris
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $88,000
- **Award type:** 5
- **Project period:** 2019-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9894787

## Citation

> US National Institutes of Health, RePORTER application 9894787, Infant Vocalizations as Early Markers of Autism Spectrum Disorder (5R03DC017944-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9894787. Licensed CC0.

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