Automated acoustic voice screening techniques for comorbid depression and anxiety disorders
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.
Authors
- David R. Thompson (ORCID: https://orcid.org/0000-0001-8518-6307)
- Kaci Cunningham
- Sonia Pulido (ORCID: https://orcid.org/0000-0003-1970-8036)
- Casey D. Calhoun (ORCID: https://orcid.org/0000-0002-2122-6135)
- Ryan Finkenbine
- Mary Pietrowicz (ORCID: https://orcid.org/0000-0001-5944-0543)
- Michael Hutchinson (ORCID: https://orcid.org/0000-0002-8675-1723)
- A.A.T.S. Reinders
- Sarah E. Donohue (ORCID: https://orcid.org/0000-0002-2790-2552)
- Anna Ford
- V. Medina
- Fiona Gruzmark
Institutions
- University of Illinois Urbana-Champaign (US)
- Illinois College (US)
- Southern Illinois University School of Medicine (US)
- University of California San Diego (US)
- National Center for Supercomputing Applications (US)
Publication Details
- Journal
- JASA Express Letters
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1121/10.0034851
- Primary Topic
- Voice and Speech Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00