A literature review on multimodal deep learning models for detecting mental disorders in conversational data: Pre-transformer and transformer-based approaches

Abstract

This paper provides a comprehensive review of multimodal deep learning models that utilize conversational data to detect mental health disorders. In addition to discussing models based on the Transformer, such as BERT (Bidirectional Encoder Representations from Transformers), this paper addresses models that existed prior to the Transformer, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The paper covers the application of these models in the construction of multimodal deep learning systems to detect mental disorders. In addition, the difficulties encountered by multimodal deep learning systems are brought up. Furthermore, the paper proposes research directions for enhancing the performance and robustness of these models in mental health applications. By shedding light on the potential of multimodal deep learning in mental health care, this paper aims to foster further research and development in this critical domain.