Segment-Based Signal Typing and Predictive Modeling in Pediatric Dysphonia With Different Vibratory Sources.

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Bibliographic Details
Title:	Segment-Based Signal Typing and Predictive Modeling in Pediatric Dysphonia With Different Vibratory Sources.
Authors:	Park, Yeonggwang¹ yppark@ucf.edu, Anand, Supraja², Baker Brehm, Susan^3,4, Kelchner, Lisa^4,5, Weinrich, Barbara^3,4, Shrivastav, Rahul⁶, de Alarcon, Alessandro⁷, Eddins, David A.¹
Source:	Journal of Speech, Language & Hearing Research. Dec2025, Vol. 68 Issue 12, p5694-5707. 14p.
Subject Terms:	Voice disorders, Automation, Prediction models, Research funding, Acoustics, Logistic regression analysis, Statistical sampling, Severity of illness index, Descriptive statistics, Human voice, Data analysis software
Abstract:	Purpose: Severe dysphonia in children often poses a challenge for conventional acoustic measurement methods due to the high degree of aperiodicity, which can result in invalid or unreliable measures. Signal typing can support the validity of these measures, but current methods rely on subjective inspection and do not account for multiple signal types within a voice sample. This study aimed to improve current signal typing practices by refining a manual signal typing tool for segment-level labeling and a predictive model for objective signal typing. Method: Sustained /α/ phonations from 94 children with a glottal vibratory source and 30 children with supraglottal vibratory source (SGVS) were evaluated by three expert speech-language pathologists using the signal typing tool. Signal type labels determined through expert consensus were considered the ground truth for each segment and used to train a predictive model. Computational measures associated with periodicity and voice quality, including pitch strength, envelope standard deviation (EnvSD8), sharpness, and smoothed cepstral peak prominence (CPPS), were extracted and used in an ordinal logistic regression model. Model performance was evaluated using a held-out test set and fivefold cross-validation. Results: Manual signal typing revealed that 11% of the overall samples and 20% of the samples with SGVS included two or more signal types. A predictive model incorporating EnvSD8, CPPS, and sharpness achieved good to excellent prediction accuracy (81%-96%) across signal types in both the test and cross-validation sets. Conclusions: The manual signal typing tool developed in this study shows promise for improving the precision of signal typing, which may enhance the reliability of conventional acoustic measures and enable the calculation of signal type proportions as potential outcome metrics. Automating signal typing using the measures investigated in this study could further increase the clinical utility of this tool by providing objective signal typing. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

Description
Abstract:	Purpose: Severe dysphonia in children often poses a challenge for conventional acoustic measurement methods due to the high degree of aperiodicity, which can result in invalid or unreliable measures. Signal typing can support the validity of these measures, but current methods rely on subjective inspection and do not account for multiple signal types within a voice sample. This study aimed to improve current signal typing practices by refining a manual signal typing tool for segment-level labeling and a predictive model for objective signal typing. Method: Sustained /α/ phonations from 94 children with a glottal vibratory source and 30 children with supraglottal vibratory source (SGVS) were evaluated by three expert speech-language pathologists using the signal typing tool. Signal type labels determined through expert consensus were considered the ground truth for each segment and used to train a predictive model. Computational measures associated with periodicity and voice quality, including pitch strength, envelope standard deviation (EnvSD8), sharpness, and smoothed cepstral peak prominence (CPPS), were extracted and used in an ordinal logistic regression model. Model performance was evaluated using a held-out test set and fivefold cross-validation. Results: Manual signal typing revealed that 11% of the overall samples and 20% of the samples with SGVS included two or more signal types. A predictive model incorporating EnvSD8, CPPS, and sharpness achieved good to excellent prediction accuracy (81%-96%) across signal types in both the test and cross-validation sets. Conclusions: The manual signal typing tool developed in this study shows promise for improving the precision of signal typing, which may enhance the reliability of conventional acoustic measures and enable the calculation of signal type proportions as potential outcome metrics. Automating signal typing using the measures investigated in this study could further increase the clinical utility of this tool by providing objective signal typing. [ABSTRACT FROM AUTHOR]
ISSN:	10924388
DOI:	10.1044/2025_JSLHR-25-00264