论文标题
通过多模式叙事和可视化检测二元对话中的抑郁症
Detecting depression in dyadic conversations with multimodal narratives and visualizations
论文作者
论文摘要
对话包含各种各样的多模式信息，这些信息为我们提供了有关演讲者的情感和情绪的暗示。在本文中，我们开发了一个支持人类分析对话的系统。我们的主要贡献是识别适当的多模式特征，并将此类特征集成到逐字对话成绩单中。我们证明了系统获得广泛的多模式信息的能力，并自动为个体的抑郁状态生成了预测评分。我们的实验表明，这种方法的性能比基线模型更好。此外，多模式叙事方法使从其他学科（例如会话分析和心理学）中融合学习变得容易。最后，这种跨学科和自动化的方法是迈向模拟从业者如何记录治疗过程以及模拟对话分析师如何手工分析对话的一步。
Conversations contain a wide spectrum of multimodal information that gives us hints about the emotions and moods of the speaker. In this paper, we developed a system that supports humans to analyze conversations. Our main contribution is the identification of appropriate multimodal features and the integration of such features into verbatim conversation transcripts. We demonstrate the ability of our system to take in a wide range of multimodal information and automatically generated a prediction score for the depression state of the individual. Our experiments showed that this approach yielded better performance than the baseline model. Furthermore, the multimodal narrative approach makes it easy to integrate learnings from other disciplines, such as conversational analysis and psychology. Lastly, this interdisciplinary and automated approach is a step towards emulating how practitioners record the course of treatment as well as emulating how conversational analysts have been analyzing conversations by hand.
