Dyadic Behavior Analysis in Depression Severity Assessment Interviews

Abstract

Previous literature suggests that depression impacts vocal timing of both participants and clinical interviewers but is mixed with respect to acoustic features. To investigate further, 57 middle-aged adults (men and women) with Major Depression Disorder and their clinical interviewers (all women) were studied. Participants were interviewed for depression severity on up to four occasions over a 21 week period using the Hamilton Rating Scale for Depression (HRSD), which is a criterion measure for depression severity in clinical trials. Acoustic features were extracted for both participants and interviewers using COVAREP Toolbox. Missing data occurred due to missed appointments, technical problems, or insufficient vocal samples. Data from 36 participants and their interviewers met criteria and were included for analysis to compare between high and low depression severity. Acoustic features for participants varied between men and women as expected, and failed to vary with depression severity for participants. For interviewers, acoustic characteristics strongly varied with severity of the interviewee’s depression. Accommodation - the tendency of interactants to adapt their communicative behavior to each other - between interviewers and interviewees was inversely related to depression severity. These findings suggest that interviewers modify their acoustic features in response to depression severity, and depression severity strongly impacts interpersonal accommodation.

Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding

“…Consistent with alternative methods, and because we were interested in severity assessment and not in diagnostic, in preliminary work we investigated a number of possible vocal features for the measurement of depression severity. We considered both frequency and timing features such as fundamental frequency (f 0 ), Maxima Dispersion Quotient (MDQ), Peak Slope (PS), Normalized Amplitude Quotient (NAQ), Quasi Open Quotient (QOQ), and switching pause durations [11], [12]. However, preliminary results showed that only switching pause durations and f 0 were correlated with depression severity [11], [12].…”

“…We considered both frequency and timing features such as fundamental frequency (f 0 ), Maxima Dispersion Quotient (MDQ), Peak Slope (PS), Normalized Amplitude Quotient (NAQ), Quasi Open Quotient (QOQ), and switching pause durations [11], [12]. However, preliminary results showed that only switching pause durations and f 0 were correlated with depression severity [11], [12]. For this reason, we used only these measures.…”

“…The latter use signal processing, computer vision, and pattern recognition methodologies. From the computer-science perspective, research has sought to identify depression from vocal utterances [8], [9], [10], [11], [12], [13], facial expression [14], [15], [16], [17], head movements/pose [18], [16], [19], body movements [18], and gaze [20]. While most research is limited to a single modality, there is increasing interest in multimodal approaches to depression detection [21], [22].…”

Automated audiovisual depression analysis

“…Recent examples from the visual channel include smaller average distance between eyelids and shorter duration of blinks [6], slower head movements [7, 8], less head motion [7–10], longer duration of looking down [7, 11], decreased smiling [8, 11], decreased frowning, and increased mouth dimpling [8]. Recent examples from the acoustic channel include increased voice tension [11], decreased coordination among formant frequencies and cepstral channels [12], longer and more variable switching pauses [4], and decreased dyadic synchrony [13]. …”

“…Studies exploring interpersonal measures are well-suited to exploring psychosocial theories; findings that depression is related to decreased dyadic synchrony [13] and longer and more variable switching pauses [4] suggest that depression interferes with interpersonal functioning in a measurable way. Of particular interest is an article by Girard et al [8], which tested the hypotheses of three clinical theories and, using automated analyses of facial expressions and head motion, found the strongest support for a novel psychosocial theory that nonverbal behavior serves to facilitate social withdrawal during periods of severe depression.…”