Abstract
In this study, a computer-driven, phoneme-agnostic method was explored for assessing speech disorders (SDs) in children, bypassing traditional labor-intensive phonetic transcription. Using the SpeechMark® automatic syllabic cluster (SC) analysis, which detects sequences of acoustic features that characterize well-formed syllables, 1952 American English utterances of 60 preschoolers were analyzed [16 with speech disorder present (SD-P) and 44 with speech disorder not present (SD-NP)] from two dialectal areas. A four-factor regression analysis evaluated the robustness of seven automated measures produced by SpeechMark® and their interactions. SCs significantly predicted SD status (p < 0.001). A secondary analysis using a generalized linear model with a negative binomial distribution evaluated the number of SCs produced by the groups. Results highlighted that children with SD-P produced fewer well-formed clusters [incidence rate ratio (IRR) = 0.8116, p ≤ 0.0137]. The interaction between speech group and age indicated that the effect of age on syllable count was more pronounced in children with SD-P (IRR = 1.0451, p = 0.0251), suggesting that even small changes in age can have a significant effect on SCs. In conclusion, speech status significantly influences the degree to which preschool children produce acoustically well-formed SCs, suggesting the potential for SCs to be speech biomarkers for SD in preschoolers.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1171-1182 |
| Number of pages | 12 |
| Journal | journal of the Acoustical Society of America |
| Volume | 156 |
| Issue number | 2 |
| DOIs | |
| State | Published - Aug 1 2024 |
Funding
We thank the parents and children who gave their time to participate in the study. We would also like to thank our undergraduate and graduate students who assisted in preparing and organizing the data for analysis. Statistical consultation and analysis support were provided by Dr. Kwang-Youn Kim from the Northwestern University Clinical and Translational Sciences Institute Biostatistics Collaboration Center and Peer Herholz from the Northwestern University Statistics and Data Science Partnership for Applied Research in Communication Sciences and Disorders. This work was sponsored by Northwestern University School of Communication and the Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Auburn University Office of the Vice President for Research and Economic Development and College of Liberal Arts (MSA), and the University of Cincinnati Department of Communication Sciences and Disorders. SpeechMark development was funded by the National Institutes of Health Grant Nos. 5R44DC010104-04 (J.M. and S.B.) and 3R44DC010104-03S1 (M.L.S.). At present SpeechMark is available without cost to researchers (and is available online).
ASJC Scopus subject areas
- Arts and Humanities (miscellaneous)
- Acoustics and Ultrasonics