The increasing demand for mental health services has highlighted the need for innovative solutions, particularly in the realm of psychological conversational AI, where the availability of sensitive data is scarce. In this work, we explored the development of a system tailored for mental health support with a novel approach to psychological assessment based on explainable emotional profiles in combination with empathetic conversational models, offering a promising tool for augmenting traditional care, particularly where immediate expertise is unavailable. Our work can be divided into two main parts, intrinsecaly connected to each other. First, we present RACLETTE, a conversational system that demonstrates superior emotional accuracy compared to state-of-the-art benchmarks in both understanding users' emotional states and generating empathetic responses during conversations, while progressively building an emotional profile of the user through their interactions. Second, we show how the emotional profiles of a user can be used as interpretable markers for mental health assessment. These profiles can be compared with characteristic emotional patterns associated with different mental disorders, providing a novel approach to preliminary screening and support.

翻译：心理健康服务需求的日益增长凸显了对创新解决方案的需求，尤其是在心理对话AI领域，该领域敏感数据的可用性十分有限。本研究探索了一种专为心理健康支持而设计的系统开发，其采用了一种基于可解释情感档案与共情对话模型相结合的新型心理评估方法，为增强传统护理提供了有前景的工具，特别是在即时专业支持不可得的情况下。我们的工作可分为两个内在关联的主要部分。首先，我们提出了RACLETTE对话系统，该系统在理解用户情感状态和生成共情回应方面均展现出优于现有基准的情感准确性，并能通过用户交互逐步构建其情感档案。其次，我们展示了如何将用户情感档案用作心理健康评估的可解释标记。这些档案可与不同精神障碍相关的特征性情感模式进行比较，从而为初步筛查与支持提供了一种创新方法。