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.
翻译:心理研究表明,抑郁情绪与情绪表达和感知密切相关,这促使人们调查情绪识别知识能否被转移用于抑郁症检测。本文使用了从情绪识别模型中提取的抑郁症检测预设特征,进一步将情感模式与音频和文字结合,形成多式抑郁症检测。拟议的情感转移提高了DAIC-WOZ的抑郁症检测性能,提高了培训稳定性。分析抑郁症患者表达的情感如何被进一步看待,为进一步理解抑郁与情感之间的关系提供了线索。