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Capturing the Intraspeaker Heterogeneity of Vocal Hyperfunction Using Spatiotemporal Indices of Relative Fundamental Frequency.

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Title: Capturing the Intraspeaker Heterogeneity of Vocal Hyperfunction Using Spatiotemporal Indices of Relative Fundamental Frequency.
Authors: Vojtech, Jenny1 jmvo@bu.edu, Toles, Laura E.2, Buckley, Daniel P.1,3, Steppa, Cara E.1,3,4
Source: Journal of Speech, Language & Hearing Research. Sep2025, Vol. 68 Issue 9, p4220-4235. 16p.
Subject Terms: *Predictive tests, *Data analysis, *Voice disorders, *Speech evaluation, *Comparative studies, Cross-sectional method, Receiver operating characteristic curves, Laryngeal muscles, Research funding, Logistic regression analysis, Signal processing, Descriptive statistics, Multivariate analysis, Physiological aspects of speech, Analysis of variance, Statistics, Human voice, Acoustic stimulation, Data analysis software, Nonparametric statistics, Sensitivity & specificity (Statistics)
Abstract: Purpose: Hyperfunctional voice disorders are highly prevalent yet difficult to characterize objectively. Relative fundamental frequency (RFF) has the potential to characterize these disorders but faces limited clinical use due to intersubject variability in mean RFF values. This study examined whether RFF variability offers insights beyond traditional mean measures. Method: Speech samples were collected from 132 adults: individuals with phonotraumatic vocal hyperfunction (PVH; n = 44), nonphonotraumatic vocal hyperfunction (NPVH; n = 44), and typical voices (controls; n = 44). Two measures of RFF variability--standard deviation and spatiotemporal index (STI)--were calculated along with mean RFF values. While standard deviation captures variability in magnitude, STI incorporates variability in time and magnitude. Permutational analyses of variance were conducted to assess relationships between group (PVH/NPVH/control) and the mean, standard deviation, and STI measures. Significant measures were entered along with demographic parameters into hierarchical multinomial logistic regression models using a training set (n = 102). Final model equations were then applied to an independent test set (n = 30) to predict group membership. Results: Mean and STI measures showed significant group differences, whereas standard deviation did not. Both mean and STI measures improved model performance after adjusting for demographics. Receiver operating characteristic analysis on the test set yielded acceptable classification (area under curve = 0.78) for group membership. Conclusions: Variability in RFF, especially when considering both time and magnitude, captures subtle features of vocal hyperfunction that may be overlooked by traditional mean measures. These findings underscore the clinical value of advanced RFF variability metrics in characterizing vocal hyperfunction. Supplemental Material: https://doi.org/10.23641/asha.29903054 [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
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  Data: Capturing the Intraspeaker Heterogeneity of Vocal Hyperfunction Using Spatiotemporal Indices of Relative Fundamental Frequency.
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  Data: <searchLink fieldCode="AR" term="%22Vojtech%2C+Jenny%22">Vojtech, Jenny</searchLink><relatesTo>1</relatesTo><i> jmvo@bu.edu</i><br /><searchLink fieldCode="AR" term="%22Toles%2C+Laura+E%2E%22">Toles, Laura E.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Buckley%2C+Daniel+P%2E%22">Buckley, Daniel P.</searchLink><relatesTo>1,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Steppa%2C+Cara+E%2E%22">Steppa, Cara E.</searchLink><relatesTo>1,3,4</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Speech%2C+Language+%26+Hearing+Research%22">Journal of Speech, Language & Hearing Research</searchLink>. Sep2025, Vol. 68 Issue 9, p4220-4235. 16p.
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  Data: *<searchLink fieldCode="DE" term="%22Predictive+tests%22">Predictive tests</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Voice+disorders%22">Voice disorders</searchLink><br />*<searchLink fieldCode="DE" term="%22Speech+evaluation%22">Speech evaluation</searchLink><br />*<searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Cross-sectional+method%22">Cross-sectional method</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink><br /><searchLink fieldCode="DE" term="%22Laryngeal+muscles%22">Laryngeal muscles</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Physiological+aspects+of+speech%22">Physiological aspects of speech</searchLink><br /><searchLink fieldCode="DE" term="%22Analysis+of+variance%22">Analysis of variance</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Human+voice%22">Human voice</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustic+stimulation%22">Acoustic stimulation</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Nonparametric+statistics%22">Nonparametric statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+%26+specificity+%28Statistics%29%22">Sensitivity & specificity (Statistics)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: Hyperfunctional voice disorders are highly prevalent yet difficult to characterize objectively. Relative fundamental frequency (RFF) has the potential to characterize these disorders but faces limited clinical use due to intersubject variability in mean RFF values. This study examined whether RFF variability offers insights beyond traditional mean measures. Method: Speech samples were collected from 132 adults: individuals with phonotraumatic vocal hyperfunction (PVH; n = 44), nonphonotraumatic vocal hyperfunction (NPVH; n = 44), and typical voices (controls; n = 44). Two measures of RFF variability--standard deviation and spatiotemporal index (STI)--were calculated along with mean RFF values. While standard deviation captures variability in magnitude, STI incorporates variability in time and magnitude. Permutational analyses of variance were conducted to assess relationships between group (PVH/NPVH/control) and the mean, standard deviation, and STI measures. Significant measures were entered along with demographic parameters into hierarchical multinomial logistic regression models using a training set (n = 102). Final model equations were then applied to an independent test set (n = 30) to predict group membership. Results: Mean and STI measures showed significant group differences, whereas standard deviation did not. Both mean and STI measures improved model performance after adjusting for demographics. Receiver operating characteristic analysis on the test set yielded acceptable classification (area under curve = 0.78) for group membership. Conclusions: Variability in RFF, especially when considering both time and magnitude, captures subtle features of vocal hyperfunction that may be overlooked by traditional mean measures. These findings underscore the clinical value of advanced RFF variability metrics in characterizing vocal hyperfunction. Supplemental Material: https://doi.org/10.23641/asha.29903054 [ABSTRACT FROM AUTHOR]
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  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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=187881680
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        Value: 10.1044/2025_JSLHR-25-00138
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      – Code: eng
        Text: English
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      – SubjectFull: Predictive tests
        Type: general
      – SubjectFull: Data analysis
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      – SubjectFull: Voice disorders
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      – SubjectFull: Speech evaluation
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      – SubjectFull: Nonparametric statistics
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      – SubjectFull: Sensitivity & specificity (Statistics)
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      – TitleFull: Capturing the Intraspeaker Heterogeneity of Vocal Hyperfunction Using Spatiotemporal Indices of Relative Fundamental Frequency.
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              Text: Sep2025
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              Y: 2025
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