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.

Entrainment, aka accommodation or alignment, is the phenomenon by which conversational partners become more similar to each other in behavior. While there has been much work on some behaviors there has been little on entrainment in speech and even less on how Spoken Dialogue Systems (SDS) which entrain to their users' speech can be created. We present an architecture and algorithm for implementing acoustic-prosodic entrainment in SDS and show that speech produced under this algorithm conforms to the feature targets, satisfying the properties of entrainment behavior observed in human-human conversations. We present results of an extrinsic evaluation of this method, comparing whether subjects are more likely to ask advice from a conversational avatar that entrains vs. one that does not, in English, Spanish and Slovak SDS.

Entrainment has been shown to occur for various linguistic features individually. Motivated by cognitive theories regarding linguistic entrainment, we analyze speakers' overall entrainment behaviors and search for an underlying structure. We consider various measures of both acoustic-prosodic and lexical entrainment, measuring the latter with a novel application of two previously introduced methods in addition to a standard high-frequency word measure. We present a negative result of our search, finding no meaningful correlations, clusters, or principal components in various entrainment measures, and discuss practical and theoretical implications.

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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.