Detecting Depression in Speech Under Different Speaking Styles and Emotional Valences

Abstract

Detecting depression in speech is a hot topic in recent years. Some inconsistent results in previous researches imply a few important influence factors are ignored. In this paper, we investigated a sample of 184 subjects (108 females, 76 males) to examine the influence of speaking style and emotional valence on depression detection. First, classification accuracy was used to measure the influence of these two factors. Then, two-way analysis of variance was employed to determine interactive acoustical features. Finally, normalized features by subtracting got higher classification accuracies. Results show that both speaking style and emotional valence are important factors. Spontaneous speech is better than automatic speech and neutral is the best choice among three emotional valences in depression detection. Normalized features improve the detection performance.