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Detecting depression in speech using verbal behavior analysis: a cross-cultural study.

Terry Amorese1, Marialucia Cuciniello1, Claudia Greco2

  • 1Department of Psychology, Università degli Studi della Campania "L. Vanvitelli", Caserta, Italy.

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Summary

Language analysis of spoken interactions can help identify depression. Depressed individuals use more first-person singular pronouns and negative emotions, but also show greater spontaneity and informal thinking compared to healthy controls.

Keywords:
cross-cultural differencesdepressionlanguage analysislinguistic contentverbal behavior analysis

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Area of Science:

  • Linguistic analysis
  • Psychiatry
  • Computational linguistics

Background:

  • Language analysis reliably differentiates depressed individuals from healthy subjects.
  • Previous research indicates linguistic markers associated with depression.

Purpose of the Study:

  • To investigate linguistic differences in spoken interactions between depressed and healthy individuals across three European regions.
  • To explore novel linguistic markers of depression, including spontaneity and thinking style.

Main Methods:

  • Analysis of speech from 241 participants (English, Italian, Russian).
  • Utilized the Linguistic Inquiry Word Count (LIWC) tool for computerized text analysis.

Main Results:

  • Depressed subjects used more first-person singular pronouns, spoke less, exhibited more negative tone, and employed more words related to negative emotions and anxiety.
  • Depressed individuals demonstrated greater spontaneity, less self-censorship, and a tendency towards informal thinking.
  • This study is the first to compare speech content of depressed participants from Western, Southern, and Eastern Europe.

Conclusions:

  • Findings can inform the development of autonomous systems for early depression detection.
  • Automated tools can assist healthcare professionals in diagnosing depression.
  • The study aims to provide cost-effective technological interventions for mental health care.