  Automatic depression detection has attracted increasing amount of attention
but remains a challenging task. Psychological research suggests that depressive
mood is closely related with emotion expression and perception, which motivates
the investigation of whether knowledge of emotion recognition can be
transferred for depression detection. This paper uses pretrained features
extracted from the emotion recognition model for depression detection, further
fuses emotion modality with audio and text to form multimodal depression
detection. The proposed emotion transfer improves depression detection
performance on DAIC-WOZ as well as increases the training stability. The
analysis of how the emotion expressed by depressed individuals is further
perceived provides clues for further understanding of the relationship between
depression and emotion.
