Data-Driven Subclassification of Speech Sound Disorders in Preschool Children Purpose The purpose of the study was to determine whether distinct subgroups of preschool children with speech sound disorders (SSD) could be identified using a subgroup discovery algorithm (SUBgroup discovery via Alternate Random Processes, or SUBARP). Of specific interest was finding evidence of a subgroup of SSD exhibiting performance consistent ... Research Article
Research Article  |   December 01, 2014
Data-Driven Subclassification of Speech Sound Disorders in Preschool Children
 
Author Affiliations & Notes
  • Jennell C. Vick
    Case Western Reserve University, Cleveland, OH
  • Thomas F. Campbell
    University of Texas at Dallas, Richardson
  • Lawrence D. Shriberg
    Waisman Center, University of Wisconsin—Madison
  • Jordan R. Green
    MGH Institute of Health Professions, Boston, MA
  • Klaus Truemper
    University of Texas at Dallas, Richardson
  • Heather Leavy Rusiewicz
    Duquesne University, Pittsburgh, PA
  • Christopher A. Moore
    U.S. Department of Veterans Affairs, Washington, DC
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication.×
  • Correspondence to Jennell C. Vick: jennell@case.edu
  • Editor: Jody Kreiman
    Editor: Jody Kreiman×
  • Associate Editor: Ben A. M. Maassen
    Associate Editor: Ben A. M. Maassen×
Article Information
Speech, Voice & Prosodic Disorders / Speech, Voice & Prosody / Speech / Research Articles
Research Article   |   December 01, 2014
Data-Driven Subclassification of Speech Sound Disorders in Preschool Children
Journal of Speech, Language, and Hearing Research, December 2014, Vol. 57, 2033-2050. doi:10.1044/2014_JSLHR-S-12-0193
History: Received June 21, 2012 , Revised May 1, 2013 , Accepted June 27, 2014
 
Journal of Speech, Language, and Hearing Research, December 2014, Vol. 57, 2033-2050. doi:10.1044/2014_JSLHR-S-12-0193
History: Received June 21, 2012; Revised May 1, 2013; Accepted June 27, 2014
Web of Science® Times Cited: 6

Purpose The purpose of the study was to determine whether distinct subgroups of preschool children with speech sound disorders (SSD) could be identified using a subgroup discovery algorithm (SUBgroup discovery via Alternate Random Processes, or SUBARP). Of specific interest was finding evidence of a subgroup of SSD exhibiting performance consistent with atypical speech motor control.

Method Ninety-seven preschool children with SSD completed speech and nonspeech tasks. Fifty-three kinematic, acoustic, and behavioral measures from these tasks were input to SUBARP.

Results Two distinct subgroups were identified from the larger sample. The 1st subgroup (76%; population prevalence estimate = 67.8%–84.8%) did not have characteristics that would suggest atypical speech motor control. The 2nd subgroup (10.3%; population prevalence estimate = 4.3%–16.5%) exhibited significantly higher variability in measures of articulatory kinematics and poor ability to imitate iambic lexical stress, suggesting atypical speech motor control. Both subgroups were consistent with classes of SSD in the Speech Disorders Classification System (SDCS; Shriberg et al., 2010a).

Conclusion Characteristics of children in the larger subgroup were consistent with the proportionally large SDCS class termed speech delay; characteristics of children in the smaller subgroup were consistent with the SDCS subtype termed motor speech disorder—not otherwise specified. The authors identified candidate measures to identify children in each of these groups.

Acknowledgments
Funding for this project was provided by the National Institute on Deafness and Other Communication Disorders (Grant R01 DC00822; principal investigator, Christopher A. Moore) and the American Speech-Language-Hearing Foundation (New Century Scholars Doctoral Scholarship to Jennell C. Vick). We are especially grateful to the participants and their families, who graciously volunteered their time for this project. In addition, we gratefully acknowledge those whose work was critical for participant recruitment, data acquisition, data extraction, and computer programming: Tammy Nash, Jill Brady, Dayna Pitcairn, Denise Balason, Stacey Pavelko, Mitzi Kweder, Katherine Moreland, Lakshmi Venkatesh, Sharon Gretz, Kevin Reilly, Roger Steeve, Kathryn Connaghan, Yumi Sumida, Alyssa Mosely, Rossella Belli, Ettore Cavallaro, Jeanette Wu, Jenny Morus, Kelsey Moore, Mary Reeves, Nicholas Moon, Dennis Tang, Adam Politis, Andrea Kettler, Laura Worthen, Dara Cohen, Heather Mabie, Rebecca Mental, Michelle Foye, and Greg Lee.
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