Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. As a result, promising depression detection systems have been reported. This paper surveys these efforts by presenting the first cross-modal review of depression detection systems and discusses best practices and most promising approaches to this task.

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The primary use of speech is in face-to-face interactions and situational context and human behavior therefore intrinsically shape and affect communication. In order to usefully model situational awareness, machines must have access to the same streams of information humans have access to. In other words, we need to provide machines with features that represent each communicative modality: face and gesture, voice and speech, and language. This paper presents OpenMM: an open-source multimodal feature extraction tool. We build upon existing open-source repositories to present the first publicly available tool for multimodal feature extraction. The tool provides a pipeline for researchers to easily extract visual and acoustic features. In addition, the tool also performs automatic speech recognition (ASR) and then uses the transcripts to extract linguistic features. We evaluate the OpenMM's multimodal feature set on deception, depression and sentiment classification tasks and show its performance is very promising. This tool provides researchers with a simple way of extracting multimodal features and consequently a richer and more robust feature representation for machine learning tasks.

Automated depression detection is inherently a multimodal problem. Therefore, it is critical that researchers investigate fusion techniques for multimodal design. This paper presents the first ever comprehensive study of fusion techniques for depression detection. In addition, we present novel linguistically-motivated fusion techniques, which we find outperform existing approaches.

This work presents the systems explored as part of the CLPsych 2019 Shared Task. More specifically, this work explores the promise of deep learning systems for suicide risk assessment.

BackgroundAttachment theory has been proven essential for mental health, including psychopathology, development, and interpersonal relationships. Validated psychometric instruments to measure attachment abound but suffer from shortcomings common to traditional psychometrics. Recent developments in multimodal fusion and machine learning pave the way for new automated and objective psychometric instruments for adult attachment that combine psychophysiological, linguistic, and behavioral analyses in the assessment of the construct.ObjectiveThe aim of this study was to present a new exposure-based, automatic, and objective adult-attachment assessment, the Biometric Attachment Test (BAT), which exposes participants to a short standardized set of visual and music stimuli, whereas their immediate reactions and verbal responses, captured by several computer sense modalities, are automatically analyzed for scoring and classification. We also aimed to empirically validate two of its assumptions: its capacity to measure attachment security and the viability of using themes as placeholders for rotating stimuli. MethodsA total of 59 French participants from the general population were assessed using the Adult Attachment Questionnaire (AAQ), the Adult Attachment Projective Picture System (AAP), and the Attachment Multiple Model Interview (AMMI) as ground truth for attachment security. They were then exposed to three different BAT stimuli sets, whereas their faces, voices, heart rate (HR), and electrodermal activity (EDA) were recorded. Psychophysiological features, such as skin-conductance response (SCR) and Bayevsky stress index; behavioral features, such as gaze and facial expressions; as well as linguistic and paralinguistic features, were automatically extracted. An exploratory analysis was conducted using correlation matrices to uncover the features that are most associated with attachment security. A confirmatory analysis was conducted by creating a single composite effects index and by testing it for correlations with attachment security. The stability of the theory-consistent features across three different stimuli sets was explored using repeated measures analysis of variances (ANOVAs).ResultsIn total, 46 theory-consistent correlations were found during the exploration (out of 65 total significant correlations). For example, attachment security as measured by the AAP was correlated with positive facial expressions (r=.36, P=.01). AMMI’s security with the father was inversely correlated with the low frequency (LF) of HRV (r=−.87, P=.03). Attachment security to partners as measured by the AAQ was inversely correlated with anger facial expression (r=−.43, P=.001). The confirmatory analysis showed that the composite effects index was significantly correlated to security in the AAP (r=.26, P=.05) and the AAQ (r=.30, P=.04) but not in the AMMI. Repeated measures ANOVAs conducted individually on each of the theory-consistent features revealed that only 7 of the 46 (15%) features had significantly different values among respo...

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