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
{"title":"Automated acoustic voice screening techniques for comorbid depression and anxiety disorders.","authors":"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","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73538,"journal":{"name":"JASA express letters","volume":"5 2","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JASA express letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1121/10.0034851","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
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.
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