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Automatic speech analysis can predict loneliness
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  • Published: 04 April 2026

Automatic speech analysis can predict loneliness

  • Diana Immel1,2,6,
  • Elisa Mallick3,
  • Nicklas Linz3,
  • Simon Barton1,2,
  • René Hurlemann1,2 &
  • …
  • Dirk Scheele4,5 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Health care
  • Psychology

Abstract

Loneliness has been demonstrated to exert a detrimental effect on mental and physical health. It may impede the formation of new social relationships by altering interactional behavior. This study provides a proof-of-concept that loneliness is reflected in altered speech, demonstrating that small yet significant effects can make loneliness audible. Samples of 96 healthy participants (mean age 31.08 years, 53 women) were recorded while they performed a picture description and storytelling task. Paralinguistic markers related to prosodic, formant, source, and temporal qualities of speech were extracted and correlated with loneliness, social anxiety and depression. To validate the diagnostic power, machine learning analyses were conducted for women and men separately. A model comprising all speech features from the picture description task significantly predicted loneliness. However, this model did not predict loneliness from the storytelling task. No single speech feature emerged as a strong predictor of loneliness. A combined model that included both speech features and psychiatric symptoms provided better predictions than psychiatric symptoms alone only in women. Overall, these findings suggest that speech offers a new perspective on how loneliness becomes perceptible to others and how it may disrupt social interactions, thereby fostering chronicity.

Data availability

The data will be provided upon reasonable request.

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Funding

Open Access funding enabled and organized by Projekt DEAL. R.H. and D.S. were supported by a grant from the German Research Foundation (DFG) (HU 1302/18 − 1 and SCHE 1913/7 − 1).

Author information

Authors and Affiliations

  1. Department of Psychiatry and Psychotherapy, School of Medicine and Health Sciences, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany

    Diana Immel, Simon Barton & René Hurlemann

  2. Karl Jaspers Clinic, Bad Zwischenahn, Germany

    Diana Immel, Simon Barton & René Hurlemann

  3. ki:elements GmbH, 66111, Saarbrücken, Germany

    Elisa Mallick & Nicklas Linz

  4. Research Center One Health Ruhr of the University Alliance Ruhr, Faculty of Medicine, Ruhr University Bochum, 44780, Bochum, Germany

    Dirk Scheele

  5. Department of Social Neuroscience, Center of Medical Psychology and Translational Neuroscience, Faculty of Medicine, Ruhr University Bochum, 44780, Bochum, Germany

    Dirk Scheele

  6. Department of Psychiatry & Psychotherapy at Karl Jaspers Clinic School of Medicine and Health Science, Carl von Ossietzky University of Oldenburg, Hermann-Ehlers-Str. 7, 26160, Bad Zwischenahn, Germany

    Diana Immel

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  1. Diana Immel
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  2. Elisa Mallick
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Contributions

D.I. and D.S. designed the experiment; D.I. performed the experiments; D.I., E.M., S.B. and N.L. analysed the data. All authors drafted the manuscript. All authors read and approved the current version of the manuscript.

Corresponding author

Correspondence to Diana Immel.

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Competing interests

Elisa Mallick is employed by the company ki: elements, which developed the application for the speech-based assessment and extracted the speech features. Nicklas Linz owns shares in the ki: elements company. Dirk Scheele, Simon Barton, Rene Hurlemann and Diana Immel have nothing to disclose.

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Immel, D., Mallick, E., Linz, N. et al. Automatic speech analysis can predict loneliness. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45965-5

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  • Received: 15 October 2025

  • Accepted: 23 March 2026

  • Published: 04 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-45965-5

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Keywords

  • Depression
  • Loneliness
  • Speech marker
  • Social anxiety
  • Speech analysis
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