A lightweight machine learning approach to detect depression from speech analysis

The growing number of people suffering from depression makes it increasingly necessary to find new approaches able to support medical experts in its diagnosis. The early detection of depressive symptoms is crucial in limiting the co-occurrence of associated behavioural disorders such as psycho-motor retardation symptoms and social withdrawal. Therefore, automatic detection systems represent promising solutions not only for supporting the early diagnosis of the disease but also for monitoring patient’s health status, thus improving both the quality of the care process and life quality of patients. At the light of these considerations, this paper proposes an automatic system exploiting a machine learning algorithm, to distinguish among depressed and healthy subjects through the analysis of selected acoustic features extracted from spontaneous speech narratives produced by healthy and depressed subjects. The proposed system achieves a classification accuracy of about 85%, proving to be a promising solution for supporting the diagnosis of depression in real-time in a reliable, fast, inexpensive and non-intrusive ways.