Preliminary detection of mild depression could immensely help in effective treatment of the common mental health disorder. Due to the lack of proper awareness and the ample mix of stigmas and misconceptions present within the society, mental health status estimation has become a truly difficult task. Due to the immense variations in character level traits from person to person, traditional deep learning methods fail to generalize in a real world setting. In our study we aim to create a human allied AI workflow which could efficiently adapt to specific users and effectively perform in real world scenarios. We propose a Hybrid deep learning approach that combines the essence of one shot learning, classical supervised deep learning methods and human allied interactions for adaptation. In order to capture maximum information and make efficient diagnosis video, audio, and text modalities are utilized. Our Hybrid Fusion model achieved a high accuracy of 96.3% on the Dataset; and attained an AUC of 0.9682 which proves its robustness in discriminating classes in complex real-world scenarios making sure that no cases of mild depression are missed during diagnosis. The proposed method is deployed in a cloud-based smartphone application for robust testing. With user-specific adaptations and state of the art methodologies, we present a state-of-the-art model with user friendly experience.

中文翻译：

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基于深度学习的混合多模态融合模型的抑郁状态估计

初步检测到轻度抑郁症可以极大地帮助有效治疗常见的精神疾病。由于缺乏适当的认识以及社会中存在大量的污名和误解，心理健康状况估计已成为一项真正困难的任务。由于人与人之间的字符水平特征存在巨大差异，因此传统的深度学习方法无法在现实世界中推广。在我们的研究中，我们旨在创建一个可以有效适应特定用户并在现实世界中有效执行的人类联盟AI工作流程。我们提出了一种混合式深度学习方法，该方法结合了一次射击学习，经典的监督式深度学习方法以及与人相关联的适应性交互的本质。为了捕获最大的信息并进行有效的诊断，必须使用视频，音频和文本形式。我们的混合融合模型在数据集上实现了96.3％的高精度；并获得0.9682的AUC，证明了其在复杂的现实世界场景中区分班级的稳健性，确保在诊断过程中不会遗漏任何轻度抑郁症。所提出的方法被部署在基于云的智能手机应用程序中以进行可靠的测试。通过针对特定用户的适应和最先进的方法，我们提供了具有用户友好体验的最新模型。9682证明了其在复杂的现实世界场景中区分班级的鲁棒性，确保在诊断过程中不会遗漏任何轻度抑郁症。所提出的方法被部署在基于云的智能手机应用程序中以进行可靠的测试。通过针对特定用户的适应和最先进的方法，我们提供了具有用户友好体验的最新模型。9682证明了其在复杂的现实世界场景中区分班级的鲁棒性，确保在诊断过程中不会遗漏任何轻度抑郁症。所提出的方法被部署在基于云的智能手机应用程序中以进行可靠的测试。通过针对特定用户的适应和最先进的方法，我们提供了具有用户友好体验的最新模型。