Title
Very early detection of Autism Spectrum Disorders based on acoustic analysis of pre-verbal vocalizations of 18-month old toddlers.
Abstract
With the increasing prevalence of Autism Spectrum Disorders (ASD), very early detection has become a key priority research topic, as early interventions can increase the chances of success. Since atypical communication is a hallmark of ASD, automated acoustic-prosodic analyses have received prominent attention. Existing studies, however, have focused on verbal children, typically over the age of three (when many children may be reliably diagnosed) and as high as early teens. Here, an acoustic-prosodic analysis of pre-verbal vocalizations (e. g., babbles, cries) of 18-month old toddlers is performed. Data was obtained from a prospective longitudinal study looking at high-risk siblings of children with ASD who were also diagnosed with ASD, as well as low-risk age-matched typically developing controls. Several acoustic-prosodic features were extracted and used to train support vector machine and probabilistic neural network classifiers; classification accuracy as high as 97% was obtained. Our findings suggest that markers of autism may be present in pre-verbal vocalizations of 18-month old toddlers, thus may be used to assist clinicians with very early detection of ASD.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6639134
ICASSP
Keywords
Field
DocType
feature extraction,medical signal processing,neural nets,paediatrics,patient diagnosis,speech recognition,support vector machines,acoustic analysis,acoustic-prosodic features,autism spectrum disorder,automated acoustic-prosodic analysis,preverbal vocalization,probabilistic neural network classifier,prospective longitudinal study,support vector machine,verbal children,Autism,PNN,SVM,biomarker,prosody
Early detection,Psychological intervention,Computer science,Artificial intelligence,Audiology,Artificial neural network,Autism,Longitudinal study,Pattern recognition,Support vector machine,Probabilistic neural network,Speech recognition,Feature extraction
Conference
ISSN
Citations 
PageRank 
1520-6149
2
0.38
References 
Authors
2
9