title: The stability and validity of automated vocal analysis in preverbal preschoolers with autism spectrum disorder.
abstract: 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.
authors.affiliation: Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.
authors.forename: Tiffany
authors.initials: T
authors.lastname: Woynaroski
authors.affiliation: School of Communication Sciences and Disorders, Institute for Intelligent Systems; University of Memphis; Konrad Lorenz Institute for Evolution and Cognition Research, Austria, Memphis, Tennessee, USA.
authors.forename: D Kimbrough
authors.initials: DK
authors.lastname: Oller
authors.affiliation: Department of Special Education, Ankara University, Ankara, Turkey.
authors.forename: Bahar
authors.initials: B
authors.lastname: Keceli-Kaysili
authors.affiliation: LENA Research Foundation, Boulder, Colorado, USA.
authors.forename: Dongxin
authors.initials: D
authors.lastname: Xu
authors.affiliation: LENA Research Foundation, Boulder, Colorado, USA.
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Richards
authors.affiliation: LENA Research Foundation, Boulder, Colorado, USA.
authors.forename: Jill
authors.initials: J
authors.lastname: Gilkerson
authors.affiliation: Nuance Communications, Burlington, MA, USA.
authors.forename: Sharmistha
authors.initials: S
authors.lastname: Gray
authors.affiliation: Special Education Department, Vanderbilt University, Nashville, Tennessee.
authors.forename: Paul
authors.initials: P
authors.lastname: Yoder
mesh_terms: D000067877:Autism Spectrum Disorder / Q000150:complications* / Q000503:physiopathology; D002663:Child Language; D002675:Child, Preschool; D003142:Communication; D005260:Female; D006801:Humans; D007805:Language Development Disorders / Q000150:complications* / Q000175:diagnosis* / Q000503:physiopathology; D008137:Longitudinal Studies; D008297:Male; D015203:Reproducibility of Results; D012984:Software
keywords: LENA; autism; automated vocal analysis; language; preschool; preverbal; useful speech; vocalizations