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Cited by 380Emotion and Mood RecognitionVoice and Speech DisordersAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology

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Mundt, James C., Snyder, Peter J., Cannizzaro, Michael S., Chappie, Kara, Geralts, Dayna S.·2007·Journal of Neurolinguistics
DOI 10.1016/j.jneuroling.2006.04.001PMC PMC3022333
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Abstract

Efforts to develop more effective depression treatments are limited by assessment methods that rely on patient-reported or clinician judgments of symptom severity. Depression also affects speech. Research suggests several objective voice acoustic measures affected by depression can be obtained reliably over the telephone. Thirty-five physician-referred patients beginning treatment for depression were assessed weekly, using standard depression severity measures, during a six-week observational study. Speech samples were also obtained over the telephone each week using an IVR system to automate data collection. Several voice acoustic measures correlated significantly with depression severity. Patients responding to treatment had significantly greater pitch variability, paused less while speaking, and spoke faster than at baseline. Patients not responding to treatment did not show similar changes. Telephone standardization for obtaining voice data was identified as a critical factor influencing the reliability and quality of speech data. This study replicates and extends previous research with a larger sample of patients assessing clinical change associated with treatment. The feasibility of obtaining voice acoustic measures reflecting depression severity and response to treatment using computer-automated telephone data collection techniques is also established. Insight and guidance for future research needs are also identified.

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Crossref · 10.1016/j.jneuroling.2006.04.001

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  • Several digital biomarkers have been investigated for MDD characterization and diagnosis, including physical activity patterns, voice features, light exposure measurements, mobile phone GPS, and normal smartphone usage duration and frequency.

    “In the recent years, several digital biomarkers have been investigated for MDD characterization and diagnosis such as measure patterns of physical activity (24, 25) features from voice samples (26, 27), light exposure measurements (25), mobile phone global positioning systems (GPS) and normal usage of smartphones such as usage duration and frequency (22).”

    From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [26]

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Authors
Mundt, J. C., Snyder, P. J., Cannizzaro, M. S., Chappie, K., & Geralts, D. S.
Affiliations
  • Healthcare Technology Systems (US)
  • University of Connecticut (US)
  • University of Vermont (US)
  • Pfizer (United States) (US)
Corresponding author
Mundt, James C.
ORCID
Venue
Journal of Neurolinguistics
Publisher
Elsevier BV
Published
2007-01
Volume
20
Issue
1
Pages
50-64
ISSN
0911-6044 (ISSN-L)
DOI
10.1016/j.jneuroling.2006.04.001
PMC
PMC3022333
OpenAlex
W2057393928
Cited by
380
References
60
Citations by year
2026: 25 · 2025: 30 · 2024: 41 · 2023: 39 · 2022: 35 · 2021: 35 · 2020: 32 · 2019: 26 · 2018: 19 · 2017: 14 · 2016: 15 · 2015: 12 · 2014: 13 · 2013: 23 · 2012: 7
FWCI
1.68
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Journal of Neurolinguistics (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institute of Mental Health (R43MH068950)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Health Sciences › Medicine › Physiology › Voice and Speech Disorders
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Keywords
depression severity, treatment response, speech analysis, interactive voice response, pitch variation, speech rate, speech pauses, automated assessment, objective symptom assessment

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