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Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine Care

Bauer JF, Gerczuk M, Schindler-Gmelch L, Amiriparian S, Ebert DD, Krajewski J, Schuller BW, Berking M (2024)

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Publication Type: Journal article

Publication year: 2024

Journal

Book Volume: 2024

Pages Range: 1-12

DOI: 10.1155/2024/9667377

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Table of Contents

Depression and Anxiety/

2024/

Article

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Abstract

Introduction

Results

Discussion

Conclusions

Data Availability

Conflicts of Interest

Acknowledgments

References

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Research Article | Open Access

Volume 2024 | Article ID 9667377 | https://doi.org/10.1155/2024/9667377

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Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine Care

Jonathan F. Bauer

,1Maurice Gerczuk

,2Lena Schindler-Gmelch

,1Shahin Amiriparian

,2David Daniel Ebert

,3Jarek Krajewski

,4Björn Schuller

,2,5and Matthias Berking

1

1Department for Clinical Psychology and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany

2Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, 86159 Augsburg, Germany

3Department for Sport and Health Sciences, Technical University Munich, 80992 Munich, Germany

4Rhenish University of Applied Science Cologne, 50676 Cologne, Germany

5Group on Language, Audio, & Music, Imperial College London, London SW7 2AZ, UK

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Academic Editor: Giulia Landi

Received

24 Mar 2023

Revised

14 Mar 2024

Accepted

22 Mar 2024

Published

09 Apr 2024

Abstract

New developments in machine learning-based analysis of speech can be hypothesized to facilitate the long-term monitoring of major depressive disorder (MDD) during and after treatment. To test this hypothesis, we collected 550 speech samples from telephone-based clinical interviews with 267 individuals in routine care. With this data, we trained and evaluated a machine learning system to identify the absence/presence of a MDD diagnosis (as assessed with the Structured Clinical Interview for DSM-IV) from paralinguistic speech characteristics. Our system classified diagnostic status of MDD with an accuracy of 66% (sensitivity: 70%, specificity: 62%). Permutation tests indicated that the machine learning system classified MDD significantly better than chance. However, deriving diagnoses from cut-off scores of common depression scales was superior to the machine learning system with an accuracy of 73% for the Hamilton Rating Scale for Depression (HRSD), 74% for the Quick Inventory of Depressive Symptomatology–Clinician version (QIDS-C), and 73% for the depression module of the Patient Health Questionnaire (PHQ-9). Moreover, training a machine learning system that incorporated both speech analysis and depression scales resulted in accuracies between 73 and 76%. Thus, while findings of the present study demonstrate that automated speech analysis shows the potential of identifying patterns of depressed speech, it does not substantially improve the validity of classifications from common depression scales. In conclusion, speech analysis may not yet be able to replace common depression scales in clinical practice, since it cannot yet provide the necessary accuracy in depression detection. This trial is registered with DRKS00023670.

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APA:

Bauer, J.F., Gerczuk, M., Schindler-Gmelch, L., Amiriparian, S., Ebert, D.D., Krajewski, J.,... Berking, M. (2024). Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine Care. Depression and Anxiety, 2024, 1-12. https://doi.org/10.1155/2024/9667377

MLA:

Bauer, Jonathan Felix, et al. "Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine Care." Depression and Anxiety 2024 (2024): 1-12.

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