Automated acoustic voice screening techniques for comorbid depression and anxiety disorders

Mary Pietrowicz; Kaci Cunningham; Dylan J. Thompson; Fiona Gruzmark; Alexis Reinders; Anna Ford; Sonia Pulido; Carmen Calhoun; Milon Hutchinson; Victor Javier Medina; Ryan Finkenbine; Sarah E. Donohue

doi:10.1121/10.0034851

Automated acoustic voice screening techniques for comorbid depression and anxiety disorders

Mary Pietrowicz, Kaci Cunningham, Dylan J. Thompson, Fiona Gruzmark, Alexis Reinders, Anna Ford, Sonia Pulido, Carmen Calhoun, Milon Hutchinson, Victor Javier Medina, Ryan Finkenbine, Sarah E. Donohue

Research output: Contribution to journal › Article › peer-review

Abstract

Anxiety disorders (AD) and major depressive disorders (MDD) are growing in prevalence, yet many people suffering from these disorders remain undiagnosed due to known perceptual, attitudinal, and structural barriers. Methods, tools, and technologies that can overcome these barriers and improve screening rates are needed. Tools based on automated analysis of acoustic voice could help bridge this gap. Comorbid AD/MDD presents additional challenges since some effects of AD and MDD oppose one another. Here, acoustic models that use acoustic and phonemic data from verbal fluency tests to discern the presence of comorbid AD/MDD are presented, with the best results of F1 = 0.83.

Original language	English (US)
Article number	024401
Journal	JASA Express Letters
Volume	5
Issue number	2
DOIs	https://doi.org/10.1121/10.0034851
State	Published - Feb 1 2025

ASJC Scopus subject areas

Acoustics and Ultrasonics
Music
Arts and Humanities (miscellaneous)

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10.1121/10.0034851

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title = "Automated acoustic voice screening techniques for comorbid depression and anxiety disorders",

abstract = "Anxiety disorders (AD) and major depressive disorders (MDD) are growing in prevalence, yet many people suffering from these disorders remain undiagnosed due to known perceptual, attitudinal, and structural barriers. Methods, tools, and technologies that can overcome these barriers and improve screening rates are needed. Tools based on automated analysis of acoustic voice could help bridge this gap. Comorbid AD/MDD presents additional challenges since some effects of AD and MDD oppose one another. Here, acoustic models that use acoustic and phonemic data from verbal fluency tests to discern the presence of comorbid AD/MDD are presented, with the best results of F1 = 0.83.",

author = "Mary Pietrowicz and Kaci Cunningham and Thompson, {Dylan J.} and Fiona Gruzmark and Alexis Reinders and Anna Ford and Sonia Pulido and Carmen Calhoun and Milon Hutchinson and Medina, {Victor Javier} and Ryan Finkenbine and Donohue, {Sarah E.}",

note = "This work was supported by the Jump ARCHES program, a collaboration between OSF HealthCare, the University of Illinois Urbana-Champaign, and the University of Illinois College of Medicine Peoria. The authors thank Conner Driver, Cory Mahler, Anvesh Jalasutrum, Dustin Pilat, Abigail Sebald, and Benjamin Finkenbine for assistance with recruitment of participants and/or data acquisition.",

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doi = "10.1121/10.0034851",

language = "English (US)",

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T1 - Automated acoustic voice screening techniques for comorbid depression and anxiety disorders

AU - Pietrowicz, Mary

AU - Cunningham, Kaci

AU - Thompson, Dylan J.

AU - Gruzmark, Fiona

AU - Reinders, Alexis

AU - Ford, Anna

AU - Pulido, Sonia

AU - Calhoun, Carmen

AU - Hutchinson, Milon

AU - Medina, Victor Javier

AU - Finkenbine, Ryan

AU - Donohue, Sarah E.

N1 - This work was supported by the Jump ARCHES program, a collaboration between OSF HealthCare, the University of Illinois Urbana-Champaign, and the University of Illinois College of Medicine Peoria. The authors thank Conner Driver, Cory Mahler, Anvesh Jalasutrum, Dustin Pilat, Abigail Sebald, and Benjamin Finkenbine for assistance with recruitment of participants and/or data acquisition.

PY - 2025/2/1

Y1 - 2025/2/1

N2 - Anxiety disorders (AD) and major depressive disorders (MDD) are growing in prevalence, yet many people suffering from these disorders remain undiagnosed due to known perceptual, attitudinal, and structural barriers. Methods, tools, and technologies that can overcome these barriers and improve screening rates are needed. Tools based on automated analysis of acoustic voice could help bridge this gap. Comorbid AD/MDD presents additional challenges since some effects of AD and MDD oppose one another. Here, acoustic models that use acoustic and phonemic data from verbal fluency tests to discern the presence of comorbid AD/MDD are presented, with the best results of F1 = 0.83.

AB - Anxiety disorders (AD) and major depressive disorders (MDD) are growing in prevalence, yet many people suffering from these disorders remain undiagnosed due to known perceptual, attitudinal, and structural barriers. Methods, tools, and technologies that can overcome these barriers and improve screening rates are needed. Tools based on automated analysis of acoustic voice could help bridge this gap. Comorbid AD/MDD presents additional challenges since some effects of AD and MDD oppose one another. Here, acoustic models that use acoustic and phonemic data from verbal fluency tests to discern the presence of comorbid AD/MDD are presented, with the best results of F1 = 0.83.

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Automated acoustic voice screening techniques for comorbid depression and anxiety disorders

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