A Study on Depression Detection Through Explainable Features of Speech

In recent years, speech as an easily accessible biomarker, has gained significant attention due to its numerous advantages and is currently a focus of active research and development in healthcare and medical fields. Due to the advancements in signal processing and deep learning technology, many studies have reported the effectiveness of using speech to detect clinical depression. However, these studies lack clinical interpretability, hindering the linkage of acoustic features to disease biology and reducing their utility for clinicians and patients. In this study, we proposed two explainable features common temporal pattern (CTP) and multi-frequency band Hurst (MFB-Hurst). These features are designed to measure clinically common symptoms of depression in speech, specifically reduced speech inflection, and noise during vocal tract closure in speech production. Based on the pronunciation data of 10 phrases, including sus-tained vowels and common expressions from healthy individuals and patients, CTP and MFB-Hurst achieved 70% and 64% accuracy in depression detection, respectively. The combination of CTP and MFB-Hurst achieved an average accuracy of 72%. These results demonstrate the potential of the proposed features in depression detection. Moreover, since these inter-pretable features are derived from clinical experience, they facilitate future applications in clinical settings.