Lian Arzbecker

Postdoctoral Researcher


Curriculum vitae


lian (at) arzbecker (dot) com


Speech Imaging Laboratory

Division of Communication Sciences, Univeristy of Wyoming



Comparing manual and automated methods for calculating speaking rate in Parkinson's disease


Journal article


Lian J. Arzbecker, Kaila L. Stipancic, Jeremy D. W. Greenlee, Kris Tjaden
JASA Express Letters, vol. 5(3), 2025, p. 035203

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APA   Click to copy
Arzbecker, L. J., Stipancic, K. L., Greenlee, J. D. W., & Tjaden, K. (2025). Comparing manual and automated methods for calculating speaking rate in Parkinson's disease . JASA Express Letters, 5(3), 035203.


Chicago/Turabian   Click to copy
Arzbecker, Lian J., Kaila L. Stipancic, Jeremy D. W. Greenlee, and Kris Tjaden. “Comparing Manual and Automated Methods for Calculating Speaking Rate in Parkinson's Disease .” JASA Express Letters 5, no. 3 (2025): 035203.


MLA   Click to copy
Arzbecker, Lian J., et al. “Comparing Manual and Automated Methods for Calculating Speaking Rate in Parkinson's Disease .” JASA Express Letters, vol. 5, no. 3, 2025, p. 035203.


BibTeX   Click to copy

@article{lian2025a,
  title = {Comparing manual and automated methods for calculating speaking rate in Parkinson's disease },
  year = {2025},
  issue = {3},
  journal = {JASA Express Letters},
  pages = {035203},
  volume = {5},
  author = {Arzbecker, Lian J. and Stipancic, Kaila L. and Greenlee, Jeremy D. W. and Tjaden, Kris}
}

Abstract

This study compared manual and automated methods for calculating speaking rate in recorded samples from individuals with Parkinson’s disease. The manual procedure involved trained researchers measuring speech rate through manual counting and acoustic analysis of speech units and pauses, while the automated method utilized iterations of a custom Praat script. Results indicated moderate agreement between methods, with the strongest agreement achieved when the automated script was optimized per speaker. Despite the limitations of a fully automated approach, this research supports the potential of automation in speaking rate analysis and provides a basis for future refinement in clinical and research contexts.



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