Evidence map›Paper›PMID 41161677›Full record

ArticleJournal of speech, language, and hearing research : JSLHR2025

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

Yeonggwang Park, Supraja Anand, Susan Baker Brehm, Lisa Kelchner, Barbara Weinrich, Rahul Shrivastav, Alessandro de Alarcon, David A Eddins

Abstract read
In one paragraph

Article in Journal of speech, language, and hearing research : JSLHR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yeonggwang ParkSchool of Communication Sciences and Disorders, University of Central Florida, Orlando.ORCID 0000-0003-0736-5371
Supraja AnandDepartment of Communication Sciences & Disorders, University of South Florida, Tampa.ORCID 0009-0007-1116-1650
Susan Baker BrehmDepartment of Speech Pathology and Audiology, Miami University, Oxford, OH.
Lisa KelchnerDivision of Speech-Language Pathology, Cincinnati Children's Hospital Medical Center, OH.ORCID 0000-0002-1592-4113
Barbara WeinrichDepartment of Speech Pathology and Audiology, Miami University, Oxford, OH.
Rahul ShrivastavOffice of the Provost & Executive Vice President, Indiana University, Bloomington.
Alessandro de AlarconPediatric Otolaryngology - Head & Neck Surgery, Cincinnati Children's Hospital Medical Center, OH.
David A EddinsSchool of Communication Sciences and Disorders, University of Central Florida, Orlando.ORCID 0000-0003-3716-2436

Funding

Cognitive and Biological Mechanisms in Pediatric Voice TherapyR01DC017923 · NIDCD · UNIVERSITY OF DELAWARE · PI VERDOLINI, KATHERINE · 2019 to 2023
$3.1M
Pediatric dysphonia: Multidisciplinary advances to improve voice quality outcomesR01DC018008 · NIDCD · UNIVERSITY OF SOUTH FLORIDA · PI DEALARCON, ALESSANDRO, EDDINS, DAVID A. · 2020 to 2024
$2.4M
NIDCD NIH HHS R01 DC017923NIDCD NIH HHS R01 DC018008
6 · The paper itself

Abstract

purposeSevere 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.

methodSustained /ɑ/ 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 (EnvSD

resultsManual 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 EnvSD

conclusionsThe 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.

Indexed as

DysphoniaSpeech AcousticsSpeech Production MeasurementAdolescentChildChild, PreschoolFemaleGlottisHumansMalePhonationReproducibility of ResultsVibrationVoice Quality

Identifiers

PMID41161677
PMCPMC12704707

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.