Acoustic vocal tremor measurement (Maryn et al., 2025)
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.
Supplemental Material S1. Information about the rate, extent, and regularity of f0 modulation and IL modulation in their respective traces extracted via fast Fourier transformation (i.e., FFT, or spectral analysis) in Praat.
Maryn, Y., Dwenger, K., Kaufmann, S., & Barkmeier-Kraemer, J. (2025). Reliability and diagnostic accuracy of semi-automated and automated acoustic quantification of vocal tremor characteristics. Journal of Speech, Language, and Hearing Research. Advance online publication. https://doi.org/10.1044/2025_JSLHR-24-00467
