{"title":"Reliability and Diagnostic Accuracy of Semi-Automated and Automated Acoustic Quantification of Vocal Tremor Characteristics.","authors":"Youri Maryn, Kaitlyn Dwenger, Sidney Kaufmann, Julie Barkmeier-Kraemer","doi":"10.1044/2025_JSLHR-24-00467","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study compared three methods of acoustic algorithm-supported extraction and analysis of vocal tremor properties (i.e., rate, extent, and regularity of intensity level and fundamental frequency modulation): (a) visual perception and manual data extraction, (b) semi-automated data extraction, and (c) fully automated data extraction.</p><p><strong>Method: </strong>Forty-five midvowel sustained [a:] and [i:] audio recordings were collected as part of a scientific project to learn about the physiologic substrates of vocal tremor. This convenience data set contained vowels with a representative variety in vocal tremor severity. First, the vocal tremor properties in intensity level and fundamental frequency tracks were visually inspected and manually measured using Praat software. Second, the vocal tremor properties were determined using two Praat scripts: automated with the script of Maryn et al. (2019) and semi-automated with an adjusted version of this script to enable the user to intervene with the signal processing. The reliability of manual vocal tremor property measurement was assessed using the intraclass correlation coefficient. The properties as measured with the two scripts (automated vs. semi-automated) were compared with the manually determined properties using correlation and diagnostic accuracy statistical methods.</p><p><strong>Results: </strong>With intraclass correlation coefficients between .770 and .914, the reliability of the manual method was acceptable. The semi-automated method correlated with manual property measures better and was more accurate in diagnosing vocal tremor than the automated method.</p><p><strong>Discussion: </strong>Manual acoustic measurement of vocal tremor properties can be laborious and time-consuming. Automated or semi-automated acoustic methods may improve efficiency in vocal tremor property measurement in clinical as well as research settings. Although both Praat script-supported methods in this study yielded acceptable validity with the manual data measurements as a referent, the semi-automated method showed the best outcomes.</p><p><strong>Supplemental material: </strong>https://doi.org/10.23641/asha.28873088.</p>","PeriodicalId":51254,"journal":{"name":"Journal of Speech Language and Hearing Research","volume":" ","pages":"2721-2740"},"PeriodicalIF":2.5000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173159/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Speech Language and Hearing Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1044/2025_JSLHR-24-00467","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/5 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: This study compared three methods of acoustic algorithm-supported extraction and analysis of vocal tremor properties (i.e., rate, extent, and regularity of intensity level and fundamental frequency modulation): (a) visual perception and manual data extraction, (b) semi-automated data extraction, and (c) fully automated data extraction.
Method: Forty-five midvowel sustained [a:] and [i:] audio recordings were collected as part of a scientific project to learn about the physiologic substrates of vocal tremor. This convenience data set contained vowels with a representative variety in vocal tremor severity. First, the vocal tremor properties in intensity level and fundamental frequency tracks were visually inspected and manually measured using Praat software. Second, the vocal tremor properties were determined using two Praat scripts: automated with the script of Maryn et al. (2019) and semi-automated with an adjusted version of this script to enable the user to intervene with the signal processing. The reliability of manual vocal tremor property measurement was assessed using the intraclass correlation coefficient. The properties as measured with the two scripts (automated vs. semi-automated) were compared with the manually determined properties using correlation and diagnostic accuracy statistical methods.
Results: With intraclass correlation coefficients between .770 and .914, the reliability of the manual method was acceptable. The semi-automated method correlated with manual property measures better and was more accurate in diagnosing vocal tremor than the automated method.
Discussion: Manual acoustic measurement of vocal tremor properties can be laborious and time-consuming. Automated or semi-automated acoustic methods may improve efficiency in vocal tremor property measurement in clinical as well as research settings. Although both Praat script-supported methods in this study yielded acceptable validity with the manual data measurements as a referent, the semi-automated method showed the best outcomes.
期刊介绍：
Mission: JSLHR publishes peer-reviewed research and other scholarly articles on the normal and disordered processes in speech, language, hearing, and related areas such as cognition, oral-motor function, and swallowing. The journal is an international outlet for both basic research on communication processes and clinical research pertaining to screening, diagnosis, and management of communication disorders as well as the etiologies and characteristics of these disorders. JSLHR seeks to advance evidence-based practice by disseminating the results of new studies as well as providing a forum for critical reviews and meta-analyses of previously published work.
Scope: The broad field of communication sciences and disorders, including speech production and perception; anatomy and physiology of speech and voice; genetics, biomechanics, and other basic sciences pertaining to human communication; mastication and swallowing; speech disorders; voice disorders; development of speech, language, or hearing in children; normal language processes; language disorders; disorders of hearing and balance; psychoacoustics; and anatomy and physiology of hearing.
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