Personalised Deep Learning for Monitoring Depressed Mood from Speech

Gerczuk M, Triantafyllopoulos A, Amiriparian S, Kathan A, Bauer JF, Berking M, Schuller B (2022)

Publication Type: Conference contribution

Publication year: 2022

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: 2022 10th E-Health and Bioengineering Conference, EHB 2022

Event location: Virtual

ISBN: 9781665485579

DOI: 10.1109/EHB55594.2022.9991737

Abstract

We utilise a longitudinal dataset of 17 526 speech samples collected from 30 patients with major depressive disorder and 11 sub-clinically depressed individuals to perform a personalised prediction of depressed mood. The data has been recorded via a smartphone app over a two-week ecological momentary assessment with three recording sessions per day. Each session's speech samples are accompanied by a self-assessed rating on the discrete visual analogue mood scale (VAMS) from 0-10. As these ratings are highly subjective, a personalised machine learning method is leveraged. For this purpose, the beginning of the recording period is utilised to train both a shared model backbone, and adapt personalised layers added at the end to each speaker's speech. Our approach yields a Spearman's correlation coefficient (ρ) of 0.79 on the test set, compared to the non-personalised baseline of ρ=0.61. Furthermore, we analyse our results with regard to the type of speech sample – reading three depression-related questions, answering them, and freely formulating an uplifting spontaneous thought. Here, we find that personalisation boosts performance across all types, especially for the fixed content question readings. Overall, our work highlights the efficacy of personalised machine learning for depressed mood monitoring.

Authors with CRIS profile

Jonathan Felix Bauer Lehrstuhl für Klinische Psychologie und Psychotherapie Matthias Berking Lehrstuhl für Klinische Psychologie und Psychotherapie

Additional Organisation(s)

Lehrstuhl für Klinische Psychologie und Psychotherapie

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Universität Augsburg DE Germany (DE)

How to cite

APA:

Gerczuk, M., Triantafyllopoulos, A., Amiriparian, S., Kathan, A., Bauer, J.F., Berking, M., & Schuller, B. (2022). Personalised Deep Learning for Monitoring Depressed Mood from Speech. In 2022 10th E-Health and Bioengineering Conference, EHB 2022. Virtual: Institute of Electrical and Electronics Engineers Inc..

MLA:

Gerczuk, Maurice, et al. "Personalised Deep Learning for Monitoring Depressed Mood from Speech." Proceedings of the 10th E-Health and Bioengineering Conference, EHB 2022, Virtual Institute of Electrical and Electronics Engineers Inc., 2022.

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