论文标题
气候和天气：通过情绪识别检查抑郁症检测
Climate and Weather: Inspecting Depression Detection via Emotion Recognition
论文作者
论文摘要
自动抑郁症检测吸引了越来越多的注意力，但仍然是一项艰巨的任务。心理学研究表明，抑郁情绪与情绪表达和感知密切相关，这激发了人们对是否可以转移情绪识别知识以进行抑郁症检测的研究。本文使用从情绪识别模型中提取的识别特征以进行抑郁症检测，进一步融合了音频和文本的情绪方式，以形成多模式抑郁症检测。提出的情绪转移改善了Daic-WoZ的抑郁症检测性能，并提高了训练稳定性。进一步感知到沮丧个体表达的情绪的分析为进一步理解抑郁和情感之间的关系提供了线索。
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.
