Paralinguistic Speach Characteristics in Major Depressive Disorder (ParaSpeChaD)

• Applicants: Professor Dr. Matthias Berking, since 10/2022; Professor Dr.-Ing. Björn Schuller
• Subject Area: Personality Psychology, Clinical and Medical Psychology, Methodology ; Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2019 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 421613952

Understanding, detecting, predicting, and effectively treating depression remain major challenges for health-care systems worldwide. Multimodal theories of depression imply that paralinguistic speech characteristics (PSCs) may play a considerable role in the somatosensoric-cognitive information processing loops that maintain depression. Consistent with this hypothesis, preliminary evidence indicates that PSCs are cross-sectionally and longitudinally associated with depression. However, the diagnostic and predictive validity of algorithms currently used to identify and utilize PSCs associated with depression is still limited. Moreover, it is yet unclear whether PSCs associated with depressions merely indicate the severity of depression, or also contribute to development and maintenance of depression. Finally, there is a lack of data on how systematically modifying PFCs may help improve the efficacy of evidence-based interventions against depression. Therefore, the proposed study - Paralinguistic Speech Characteristics in Major Depressive Disorder (ParaSpeChaD) - aims to (a) significantly improve PSC-based algorithms for detecting and predicting the future course of depression, (b) clarify causal effects of PSCs on depressive symptoms, and (c) explore the clinical potential of PSC-based interventions. To attain these aims, we will use cross-sectional and longitudinal data collected in preparation for ParaSpeChaD and newly collected data within the proposed study to further improve presently used algorithms identifying depressogenic PSCs. Using these improved algorithms, we will compare clinically depressed individuals and non-depressed controls with regard to PSCs in neutral and reappraisal-related reading probes. Additionally, we will conduct an experiment testing the effects of PSC-feedback on the efficacy of reappraisal in clinically depressed and subthreshold individuals. Based on the insights gained in ParaSpeChaD, we will finally develop a PSC-feedback-enhanced reappraisal training against depression and explore feasibility, acceptance and therapeutic potential of this training in a pilot trial. Findings from the proposed study will elucidate the role of PSCs as a maintaining factor of depression, improve the validity of economic and non-invasive ways of detecting depression and predicting its future course, and provide preliminary data on the therapeutic potential of PSC-based interventions.

• DFG Programme: Research Grants
• International Connection: Australia
• Co-Investigators: Andreas Ahnert, Ph.D.; Professor Dr. Johannes Kornhuber; Professor Dr. Oliver Schultheiss
• Cooperation Partner: Professor Julien Epps, Ph.D.
• Ehemaliger Antragsteller: Professor Dr. Jarek Krajewski, until 10/2022