Characterising depressed speech for classification

Depression is a serious psychiatric disorder that affects mood, thoughts, and the ability to function in everyday life. This paper investigates the characteristics of depressed speech for the purpose of automatic classification by analysing the effect of different speech features on the classification results. We analysed voiced, unvoiced and mixed speech in order to gain a better understanding of depressed speech and to bridge the gap between physiological and affective computing studies. This understanding may ultimately lead to an objective affective sensing system that supports clinicians in their diagnosis and monitoring of clinical depression. The characteristics of depressed speech were statistically analysed using ANOVA and linked to their classification results using GMM and SVM. Features were extracted and classified over speech utterances of 30 clinically depressed patients against 30 controls (both gender-matched) in a speaker-independent manner. Most feature classification results were consistent with their statistical characteristics, providing a link between physiological and affective computing studies. The classification results from low-level features were slightly better than the statistical functional features, which indicates a loss of information in the latter. We found that both mixed and unvoiced speech were as useful in detecting depression as voiced speech, if not better.