Objective Methods for Reliable Detection of Concealed Depression

Abstract

Recent research has shown that it is possible to automatically detect clinical depression from audiovisual recordings. Before considering integration in a clinical pathway, a key question that must be asked is whether such systems can be easily fooled. This work explores the potential of acoustic features to detect clinical depression in adults both when acting normally and when asked to conceal their depression. Nine adults diagnosed with mild to moderate depression as per the Beck Depression Inventory (BDI-II) and Patient Health Questionnaire (PHQ, Chang, 2012) were asked a series of questions and to read a excerpt from a novel aloud under two different experimental conditions. In one, participants were asked to act naturally and in the other, to suppress anything that they felt would be indicative of their depression. Acoustic features were then extracted from this data and analyzed using paired t-tests to determine any statistically significant differences between healthy and depressed participants. Most features that were found to be significantly different during normal behavior remained so during concealed behavior. In leave-one-subject-out automatic classification studies of the 9 depressed subjects and 8 matched healthy controls, an 88% classification accuracy and 89% sensitivity was achieved. Results remained relatively robust during concealed behavior, with classifiers trained on only non-concealed data achieving 81% detection accuracy and 75% sensitivity when tested on concealed data. These results indicate there is good potential to build deception-proof automatic depression monitoring systems.

Acoustic differences between healthy and depressed people: a cross-situation study

“…The results of MANCOVA reported 12 valid main effects of group with large effect sizes, which means the vocal abnormalities in depressed people exist in various emotional or cognitive scenarios. Compared with the previous studies that usually compared among different tasks [5, 10, 14], we set up more multiple speech scenarios that included more diverse tasks (represented different cognitive demands) and added another influential variable emotion, while excluding the covariates. Therefore, our study provides more reliable evidence of the cross-situational vocal abnormalities in depressed people.…”

“…The studies about voice analysis of depression designed various tasks (details about the tasks are shown in Additional file 3), including: 1) interview, usually originating from interview [3, 7, 8, 18–20]; 2) natural speech, in general referring to daily talk or man-machine conversation [10, 21]; 3) describe or comment picture [1, 22]; and 4) reading, normally conducted by text [5, 6, 9, 10, 23]. In addition, video is a stimulus that is commonly utilized for evoking emotion [24, 25] and could be regarded as a task in our study.…”

“…Researchers identified a salient correlation between F0 and severity of depression [4, 5, 7, 27]. Loudness has an obvious negative relationship with the rating of depression [6, 21], and the loudness of depressed people is significantly lower than that of healthy people [1, 10]. Furthermore, some studies [28–30] showed that MFCCs can be used to identify depression.…”

“…Empirical studies also revealed that acoustic features have significant relationships with the rating of depression [3–6]. Additionally, they can be utilized for distinguishing depressed people from healthy ones [7–10]. Moreover, the differences of acoustic features between depressed and healthy people have shown relatively high stability over time [11].…”

“…The second variable is emotion . One study [10] investigated the vocal expression of depressed people in two emotional situations: conceal and non-conceal emotion. Their results indicated that vocal abnormalities in people with depression existed in both conceal and non-conceal conditions.…”