Journal article
Lian J. Arzbecker, Kris Tjaden
Journal of the Acoustical Society of America
Journal of the Acoustical Society of America
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Cite
APA
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Arzbecker, L. J., & Tjaden, K. Validating automated speech timing methods across sentence , paragraph, and monologue tasks (submitted). Journal of the Acoustical Society of America.
Chicago/Turabian
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Arzbecker, Lian J., and Kris Tjaden. “Validating Automated Speech Timing Methods across Sentence , Paragraph, and Monologue Tasks (Submitted).” Journal of the Acoustical Society of America (n.d.).
MLA
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Arzbecker, Lian J., and Kris Tjaden. “Validating Automated Speech Timing Methods across Sentence , Paragraph, and Monologue Tasks (Submitted).” Journal of the Acoustical Society of America.
BibTeX Click to copy
@article{lian-a,
title = {Validating automated speech timing methods across sentence , paragraph, and monologue tasks (submitted)},
journal = {Journal of the Acoustical Society of America},
author = {Arzbecker, Lian J. and Tjaden, Kris}
}
Abstract
(submitted)
Automated measurement of speaking and articulation rates offers a scalable alternative to manual analysis in motor speech disorders. This study evaluated a Praat-based script that estimates global speech timing by detecting syllable nuclei via amplitude dips. In speakers with Parkinson’s Disease, Multiple Sclerosis, and healthy controls, speaking rate (syllables/total duration) and articulation rate (syllables/speaking time) were measured manually and with an automated script. Sixty participants (20 per group) completed sentence, paragraph, and monologue tasks (N = 180 recordings). Default script parameters were also compared to an optimized version with manually tuned peak dip thresholds. Analyses included error metrics, linear mixed-effects models, and generalizability analysis. Speaking rate showed strong correlations with manual measures across all groups and tasks (r = .623–.998). However, default automated estimates moderately underestimated both rate metrics, especially in clinical speakers and for the monologue task. Articulation rate was more sensitive to measurement method, accounting for nearly half of total variance. Optimization of the Praat script parameters reduced proportional error by ~60%, with varying effects across groups. Optimized automated methods can improve measurement accuracy, but population- and task-specific challenges persist, especially for articulation rate in MS and PD speakers.
Automated measurement of speaking and articulation rates offers a scalable alternative to manual analysis in motor speech disorders. This study evaluated a Praat-based script that estimates global speech timing by detecting syllable nuclei via amplitude dips. In speakers with Parkinson’s Disease, Multiple Sclerosis, and healthy controls, speaking rate (syllables/total duration) and articulation rate (syllables/speaking time) were measured manually and with an automated script. Sixty participants (20 per group) completed sentence, paragraph, and monologue tasks (N = 180 recordings). Default script parameters were also compared to an optimized version with manually tuned peak dip thresholds. Analyses included error metrics, linear mixed-effects models, and generalizability analysis. Speaking rate showed strong correlations with manual measures across all groups and tasks (r = .623–.998). However, default automated estimates moderately underestimated both rate metrics, especially in clinical speakers and for the monologue task. Articulation rate was more sensitive to measurement method, accounting for nearly half of total variance. Optimization of the Praat script parameters reduced proportional error by ~60%, with varying effects across groups. Optimized automated methods can improve measurement accuracy, but population- and task-specific challenges persist, especially for articulation rate in MS and PD speakers.