Journal of speech, language, and hearing research : JSLHR · 2025
Segment-Based Signal Typing and Predictive Modeling in Pediatric Dysphonia With Different Vibratory Sources.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Park Yeonggwang, Anand Supraja, Brehm Susan Baker, Kelchner Lisa, Weinrich Barbara, Shrivastav Rahul, de Alarcon Alessandro, Eddins David A
Аннотация
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 /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 (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.
METHOD: Sustained /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 (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.