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.

通过使用预训练的情感识别模型进行特征提取来实现自动抑郁症检测，结合音频和文本的情感信息，提出了一种情感传递方法来增加训练的稳定性和检测性能，在进一步理解抑郁症与情感关系方面提供了线索。