Noise Robust Recognition of Depression Status and Treatment Response from Speech via Unsupervised Feature Aggregation

Gerczuk M, Amiriparian S, Kathan A, Bauer J, Berking M, Schuller BW (2023)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2023

Journal

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Event location: Sydney, NSW AU

ISBN: 979-8-3503-2448-8

DOI: 10.1109/EMBC40787.2023.10340985

Abstract

In the presented work, we utilise a noisy dataset of clinical interviews with depression patients conducted over the telephone for the purpose of depression classification and automated detection of treatment response. Compared to most previous studies dealing with depression recognition from speech, our data set does not include a healthy group of subjects that have never been diagnosed with depression. Furthermore, it contains measurements at different time points for individual subjects, making it suitable for machine learning-based detection of treatment response. In our experiments, we make use of an unsupervised feature quantisation and aggregation method achieving 69.2% Unweighted Average Recall (UAR) when classifying whether patients are currently in remission or experiencing a major depressive episode (MDE). The performance of our model matches cutoff-based classification via Hamilton Rating Scale for Depression (HRSD) scores. Finally, we show that using speech samples, we can detect response to treatment with a UAR of 68.1%.

Authors with CRIS profile

Jonathan Bauer Lehrstuhl für Klinische Psychologie und Psychotherapie (KliPs) Matthias Berking Lehrstuhl für Klinische Psychologie und Psychotherapie (KliPs)

Involved external institutions

Universität Augsburg DE Germany (DE)

How to cite

APA:

Gerczuk, M., Amiriparian, S., Kathan, A., Bauer, J., Berking, M., & Schuller, B.W. (2023). Noise Robust Recognition of Depression Status and Treatment Response from Speech via Unsupervised Feature Aggregation. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Sydney, NSW, AU: Institute of Electrical and Electronics Engineers Inc..

MLA:

Gerczuk, Maurice, et al. "Noise Robust Recognition of Depression Status and Treatment Response from Speech via Unsupervised Feature Aggregation." Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023, Sydney, NSW Institute of Electrical and Electronics Engineers Inc., 2023.

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