Quantifying vocal subsystems of adults with minimally verbal autism spectrum disorder

The understanding of speech production and its fine-motor underpinnings in individuals with autism spectrum disorder (ASD) has become an area of growing interest in clinical research. Here, we developed objective acoustic-based measures to characterize and contrast the respiratory, laryngeal, and articulatory vocal production subsystems based on data from 27 adults with minimally-verbal autism spectrum disorder (mv-ASD) relative to 27 age-matched neurotypical (NT) peers. The complexity of representative movement dynamics of each subsystem was determined through signal correlation analysis, associated with degrees of freedom derived from correlation structure. As proxies to underlying speech production, we used base features of signal envelope, pitch and formant trajectories associated with each subsystem, respectively, along with their velocities. In addition, we examined cepstral peak prominence and mel-frequency cepstral coefficients, associated with laryngeal and articulatory systems, widely used in assessing voice and speech in neurological and pathological conditions. With speech data from a protocol consisting of diadochokinetic sequencing (DDK), and three different single-word tasks of varying cognitive load (Imitation, Naming, Reading), we studied complexity of motor dynamics at group and individual levels under each task condition. At group level, mv-ASDs showed lower complexity than NT participants for the respiratory and laryngeal subsystems and, except for the DDK task, higher complexity for the articulatory subsystem. At the individual level, there is a range of complexity consistent with mv-ASD heterogeneity. Using Pearson and Spearman correlation measures, we correlated our subsystem characterization with measures of non-verbal IQ, expressive and receptive vocabulary, visual-motor integration, and fine motor dexterity to understand the clinical relevance of these acoustic features, as well as the effect of outliers. When combining mv-ASD and NT groups, articulatory base features were most consistent in correlating with expressive vocabulary across tasks, compared to respiratory and laryngeal features. The Pearson-based association holds within the mv-ASD group alone for the single-word tasks. Continued understanding of speech and language challenges of individuals with mv-ASD reflected in metrics of each of the three speech production subsystems (respiratory, laryngeal, and articulatory) and their relationship with standardized motor, cognitive, and language assessments, will promote design of functionally-tailored personalized and clinically acceptable interventions.

Authors

Institutions

Publication Details

Journal
Frontiers in Human Neuroscience
Published
2026-03-23
DOI
https://doi.org/10.3389/fnhum.2026.1695265
Primary Topic
Autism Spectrum Disorder Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantifying vocal subsystems of adults with minimally verbal autism spectrum disorder

Pattie Maes, Thomas F. Quatieri, Sophia Yuditskaya, Tanya Talkar et al.
Frontiers in Human Neuroscience
Autism Spectrum Disorder Research
article

Quantifying vocal subsystems of adults with minimally verbal autism spectrum disorder

Pattie Maes, Thomas F. Quatieri, Sophia Yuditskaya, Tanya Talkar, Nataliya Kosmyna, Lisa Nowinski, Maria Mody, Meredith Pecukonis, Paige Hickey Townsend, Laura Sarnie, Nishat Protyasha, Williamson, Chakameh Jafari, Christopher J. McDougle
article en

Abstract

The understanding of speech production and its fine-motor underpinnings in individuals with autism spectrum disorder (ASD) has become an area of growing interest in clinical research. Here, we developed objective acoustic-based measures to characterize and contrast the respiratory, laryngeal, and articulatory vocal production subsystems based on data from 27 adults with minimally-verbal autism spectrum disorder (mv-ASD) relative to 27 age-matched neurotypical (NT) peers. The complexity of representative movement dynamics of each subsystem was determined through signal correlation analysis, associated with degrees of freedom derived from correlation structure. As proxies to underlying speech production, we used base features of signal envelope, pitch and formant trajectories associated with each subsystem, respectively, along with their velocities. In addition, we examined cepstral peak prominence and mel-frequency cepstral coefficients, associated with laryngeal and articulatory systems, widely used in assessing voice and speech in neurological and pathological conditions. With speech data from a protocol consisting of diadochokinetic sequencing (DDK), and three different single-word tasks of varying cognitive load (Imitation, Naming, Reading), we studied complexity of motor dynamics at group and individual levels under each task condition. At group level, mv-ASDs showed lower complexity than NT participants for the respiratory and laryngeal subsystems and, except for the DDK task, higher complexity for the articulatory subsystem. At the individual level, there is a range of complexity consistent with mv-ASD heterogeneity. Using Pearson and Spearman correlation measures, we correlated our subsystem characterization with measures of non-verbal IQ, expressive and receptive vocabulary, visual-motor integration, and fine motor dexterity to understand the clinical relevance of these acoustic features, as well as the effect of outliers. When combining mv-ASD and NT groups, articulatory base features were most consistent in correlating with expressive vocabulary across tasks, compared to respiratory and laryngeal features. The Pearson-based association holds within the mv-ASD group alone for the single-word tasks. Continued understanding of speech and language challenges of individuals with mv-ASD reflected in metrics of each of the three speech production subsystems (respiratory, laryngeal, and articulatory) and their relationship with standardized motor, cognitive, and language assessments, will promote design of functionally-tailored personalized and clinically acceptable interventions.

Frontiers in Human NeuroscienceVol. 20
MIT Lincoln Laboratory (US), Athinoula A. Martinos Center for Biomedical Imaging (US), Human Media (US), Center for Autism and Related Disorders (US)
Quality Education, Peace, Justice and strong institutions
Openalex Percentile: Top 15%
Autism Spectrum Disorder Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.