Segment-Based Signal Typing and Predictive Modeling in Pediatric Dysphonia With Different Vibratory Sources.
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| Title: | Segment-Based Signal Typing and Predictive Modeling in Pediatric Dysphonia With Different Vibratory Sources. |
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| Authors: | Park, Yeonggwang1 yppark@ucf.edu, Anand, Supraja2, Baker Brehm, Susan3,4, Kelchner, Lisa4,5, Weinrich, Barbara3,4, 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] |
| Copyright of Journal of Speech, Language & Hearing Research is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 190171404 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Segment-Based Signal Typing and Predictive Modeling in Pediatric Dysphonia With Different Vibratory Sources. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Park%2C+Yeonggwang%22">Park, Yeonggwang</searchLink><relatesTo>1</relatesTo><i> yppark@ucf.edu</i><br /><searchLink fieldCode="AR" term="%22Anand%2C+Supraja%22">Anand, Supraja</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Baker+Brehm%2C+Susan%22">Baker Brehm, Susan</searchLink><relatesTo>3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Kelchner%2C+Lisa%22">Kelchner, Lisa</searchLink><relatesTo>4,5</relatesTo><br /><searchLink fieldCode="AR" term="%22Weinrich%2C+Barbara%22">Weinrich, Barbara</searchLink><relatesTo>3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Shrivastav%2C+Rahul%22">Shrivastav, Rahul</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22de+Alarcon%2C+Alessandro%22">de Alarcon, Alessandro</searchLink><relatesTo>7</relatesTo><br /><searchLink fieldCode="AR" term="%22Eddins%2C+David+A%2E%22">Eddins, David A.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Speech%2C+Language+%26+Hearing+Research%22">Journal of Speech, Language & Hearing Research</searchLink>. Dec2025, Vol. 68 Issue 12, p5694-5707. 14p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Voice+disorders%22">Voice disorders</searchLink><br />*<searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustics%22">Acoustics</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+sampling%22">Statistical sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Severity+of+illness+index%22">Severity of illness index</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Human+voice%22">Human voice</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Speech, Language & Hearing Research is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1044/2025_JSLHR-25-00264 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 5694 Subjects: – SubjectFull: Voice disorders Type: general – SubjectFull: Automation Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Research funding Type: general – SubjectFull: Acoustics Type: general – SubjectFull: Logistic regression analysis Type: general – SubjectFull: Statistical sampling Type: general – SubjectFull: Severity of illness index Type: general – SubjectFull: Descriptive statistics Type: general – SubjectFull: Human voice Type: general – SubjectFull: Data analysis software Type: general Titles: – TitleFull: Segment-Based Signal Typing and Predictive Modeling in Pediatric Dysphonia With Different Vibratory Sources. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Park, Yeonggwang – PersonEntity: Name: NameFull: Anand, Supraja – PersonEntity: Name: NameFull: Baker Brehm, Susan – PersonEntity: Name: NameFull: Kelchner, Lisa – PersonEntity: Name: NameFull: Weinrich, Barbara – PersonEntity: Name: NameFull: Shrivastav, Rahul – PersonEntity: Name: NameFull: de Alarcon, Alessandro – PersonEntity: Name: NameFull: Eddins, David A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10924388 Numbering: – Type: volume Value: 68 – Type: issue Value: 12 Titles: – TitleFull: Journal of Speech, Language & Hearing Research Type: main |
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