Predicting Speech Intelligibility With a Multiple Speech Subsystems Approach in Children With Cerebral Palsy Purpose Speech acoustic characteristics of children with cerebral palsy (CP) were examined with a multiple speech subsystems approach; speech intelligibility was evaluated using a prediction model in which acoustic measures were selected to represent three speech subsystems. Method Nine acoustic variables reflecting different subsystems, and speech intelligibility, were measured ... Research Article
Research Article  |   October 01, 2014
Predicting Speech Intelligibility With a Multiple Speech Subsystems Approach in Children With Cerebral Palsy
 
Author Affiliations & Notes
  • Jimin Lee
    University of Wisconsin—Madison
  • Katherine C. Hustad
    University of Wisconsin—Madison
  • Gary Weismer
    University of Wisconsin—Madison
  • Jimin Lee is now at The Pennsylvania State University.
    Jimin Lee is now at The Pennsylvania State University.×
  • 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 Jimin Lee: JXL91@psu.edu
  • Editor: Jody Kreiman
    Editor: Jody Kreiman×
  • Associate Editor: Julie Liss
    Associate Editor: Julie Liss×
Article Information
Speech, Voice & Prosodic Disorders / Special Populations / Genetic & Congenital Disorders / Speech / Research Articles
Research Article   |   October 01, 2014
Predicting Speech Intelligibility With a Multiple Speech Subsystems Approach in Children With Cerebral Palsy
Journal of Speech, Language, and Hearing Research, October 2014, Vol. 57, 1666-1678. doi:10.1044/2014_JSLHR-S-13-0292
History: Received October 23, 2013 , Revised April 8, 2014 , Accepted May 8, 2014
 
Journal of Speech, Language, and Hearing Research, October 2014, Vol. 57, 1666-1678. doi:10.1044/2014_JSLHR-S-13-0292
History: Received October 23, 2013; Revised April 8, 2014; Accepted May 8, 2014
Web of Science® Times Cited: 5

Purpose Speech acoustic characteristics of children with cerebral palsy (CP) were examined with a multiple speech subsystems approach; speech intelligibility was evaluated using a prediction model in which acoustic measures were selected to represent three speech subsystems.

Method Nine acoustic variables reflecting different subsystems, and speech intelligibility, were measured in 22 children with CP. These children included 13 with a clinical diagnosis of dysarthria (speech motor impairment [SMI] group) and 9 judged to be free of dysarthria (no SMI [NSMI] group). Data from children with CP were compared to data from age-matched typically developing children.

Results Multiple acoustic variables reflecting the articulatory subsystem were different in the SMI group, compared to the NSMI and typically developing groups. A significant speech intelligibility prediction model was obtained with all variables entered into the model (adjusted R 2 = .801). The articulatory subsystem showed the most substantial independent contribution (58%) to speech intelligibility. Incremental R 2 analyses revealed that any single variable explained less than 9% of speech intelligibility variability.

Conclusions Children in the SMI group had articulatory subsystem problems as indexed by acoustic measures. As in the adult literature, the articulatory subsystem makes the primary contribution to speech intelligibility variance in dysarthria, with minimal or no contribution from other systems.

Acknowledgments
Portions of these data are from Jimin Lee's dissertation and were presented at the 2010 Biennial Conference on Motor Speech, Savannah, GA. This research was funded by Grants K23DC007114 and R01DC009411 from the National Institute on Deafness and Other Communication Disorders and by a New Century Scholars Doctoral Scholarship from the American Speech-Language-Hearing Foundation. Support was also provided by Grant P30HD03352 to the Waisman Center at the University of Wisconsin—Madison from the National Institute of Child Health and Human Development.
We thank Kris Gorton, Ok-Bun Lee, Kelly McCourt Hayes, Therese Wycklendt, and Amy Kramper for assistance with data collection and data analysis.
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