Access the full text.
Sign up today, get DeepDyve free for 14 days.
K. Temple (2007)
A randomized comparison of the effect of two prelinguistic communication interventions on the acquisition of spoken communication in preschoolers with ASDChild Care Health and Development, 33
F. Tion, J. Gilkerson, J. Richards (2009)
The LENA Natural Language Study
(2003)
MacArthur communicative development inventories: User's guide and technical manual
S. Sheinkopf, P. Mundy, D. Oller, M. Steffens (2000)
Vocal Atypicalities of Preverbal Autistic ChildrenJournal of Autism and Developmental Disorders, 30
J. Steiger (1980)
Tests for comparing elements of a correlation matrix.Psychological Bulletin, 87
Dongxin Xu, J. Richards, J. Gilkerson (2014)
Automated analysis of child phonetic production using naturalistic recordings.Journal of speech, language, and hearing research : JSLHR, 57 5
P. Yoder, L. Watson, W. Lambert (2014)
Value-Added Predictors of Expressive and Receptive Language Growth in Initially Nonverbal Preschoolers with Autism Spectrum DisordersJournal of Autism and Developmental Disorders, 45
R. McCrae, J. Kurtz, S. Yamagata, A. Terracciano (2011)
Internal Consistency, Retest Reliability, and Their Implications for Personality Scale ValidityPersonality and Social Psychology Review, 15
L. Eisenberg (1956)
The autistic child in adolescence.The American journal of psychiatry, 112 8
(2013)
Calculation for the test of the difference between two dependent correlations with one variable in common
Craig Enders (2011)
Analyzing longitudinal data with missing values.Rehabilitation psychology, 56 4
(2014)
LENA : Every word counts
S. Bradley-Johnson (1997)
Mullen Scales of Early LearningPsychology in the Schools, 34
D. Oller, P. Niyogi, S. Gray, J. Richards, J. Gilkerson, Dong Xu, U. Yapanel, S. Warren (2010)
Automated vocal analysis of naturalistic recordings from children with autism, language delay, and typical developmentProceedings of the National Academy of Sciences, 107
H. Tager-Flusberg, R. Paul, C. Lord (2014)
Language and Communication in Autism
P. Yoder, F. Symons (2010)
Observational Measurement of Behavior
Allison Plumb, A. Wetherby (2013)
Vocalization development in toddlers with autism spectrum disorder.Journal of speech, language, and hearing research : JSLHR, 56 2
J. Rushton, C. Brainerd, M. Pressley, P. Rushton (1983)
Behavioral Development and Construct Validity: The Principle of AggregationPsychological Bulletin, 94
(2000)
Diagnostic and statistical manual of mental disorders-IV-TR
J. Richards, J. Gilkerson, T. Paul, Dongxin Xu (2009)
The LENA Automatic Vocalization Assessment
E. Billstedt, I. Gillberg, Christopher Gillberg, C. Gillberg (2007)
Autism in adults: symptom patterns and early childhood predictors. Use of the DISCO in a community sample followed from childhood.Journal of child psychology and psychiatry, and allied disciplines, 48 11
Micheal Sandbank, P. Yoder (2014)
Measuring Representative Communication in Young Children With Developmental DelayTopics in Early Childhood Special Education, 34
E. Billstedt, I. Carina Gillberg, C. Gillberg (2007)
Autism in adults: Symptom patterns and early childhood predictors, 48
Stacy Shumway, A. Wetherby (2009)
Communicative acts of children with autism spectrum disorders in the second year of life.Journal of speech, language, and hearing research : JSLHR, 52 5
C. Gillberg, S. Steffenburg (1987)
Outcome and prognostic factors in infantile autism and similar conditions: A population-based study of 46 cases followed through pubertyJournal of Autism and Developmental Disorders, 17
E. Nick (1977)
Applied multiple regression, 29
P. Yoder, D. Oller, J. Richards, S. Gray, J. Gilkerson (2013)
Stability and Validity of an Automated Measure of Vocal Development From Day‐Long Samples in Children With and Without Autism Spectrum DisorderAutism Research, 6
D. Oller (2000)
The emergence of the speech capacity
D. Adler (2016)
Using Multivariate Statistics
J. Gilkerson, J. Richards (2008)
The LENA natural language study (LENA Technical Report 02‐2)
P. Yoder, W. Stone (2006)
A randomized comparison of the effect of two prelinguistic communication interventions on the acquisition of spoken communication in preschoolers with ASD.Journal of speech, language, and hearing research : JSLHR, 49 4
K. Gotham, S. Risi, A. Pickles, C. Lord (2007)
The Autism Diagnostic Observation Schedule: Revised Algorithms for Improved Diagnostic ValidityJournal of Autism and Developmental Disorders, 37
A. Wetherby, N. Watt, Lindee Morgan, Stacy Shumway (2007)
Social Communication Profiles of Children with Autism Spectrum Disorders Late in the Second Year of LifeJournal of Autism and Developmental Disorders, 37
L. Crocker, J. Algina (1986)
Introduction to Classical and Modern Test Theory
C. Lord, S. Risi, Linda Lambrecht, E. Cook, B. Leventhal, Pamela DiLavore, A. Pickles, M. Rutter (2000)
The Autism Diagnostic Observation Schedule—Generic: A Standard Measure of Social and Communication Deficits Associated with the Spectrum of AutismJournal of Autism and Developmental Disorders, 30
Theory and research suggest that vocal development predicts “useful speech” in preschoolers with autism spectrum disorder (ASD), but conventional methods for measurement of vocal development are costly and time consuming. This longitudinal correlational study examines the reliability and validity of several automated indices of vocalization development relative to an index derived from human coded, conventional communication samples in a sample of preverbal preschoolers with ASD. Automated indices of vocal development were derived using software that is presently “in development” and/or only available for research purposes and using commercially available Language ENvironment Analysis (LENA) software. Indices of vocal development that could be derived using the software available for research purposes: (a) were highly stable with a single day‐long audio recording, (b) predicted future spoken vocabulary to a degree that was nonsignificantly different from the index derived from conventional communication samples, and (c) continued to predict future spoken vocabulary even after controlling for concurrent vocabulary in our sample. The score derived from standard LENA software was similarly stable, but was not significantly correlated with future spoken vocabulary. Findings suggest that automated vocal analysis is a valid and reliable alternative to time intensive and expensive conventional communication samples for measurement of vocal development of preverbal preschoolers with ASD in research and clinical practice. Autism Res 2017, 10: 508–519. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.
Autism Research – Wiley
Published: Mar 1, 2017
Keywords: ; ; ; ; ; ; ;
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.