FusionNet: A Multi-Modal Deep Learning Framework for Emotion Recognition and Mental Health Assessment

Today, especially with an increasing percentage relying on digital tools for early detection and monitoring, assessment of mental health holds a very high importance. Generally, the classic emotion identification algorithms that limit their assessment to one modality, voice, or text, often ignore the subtle and complex human emotions. In this paper, for the first time, a new multi-modal deep learning architecture, called Fusion Net, is proposed for dependable emotion recognition that combines the textual, auditory, and visual modalities. The proposed system utilizes convolutional neural networks (CNNs) for facial expression detection, recurrent neural networks (RNNs) for audio signal processing, and high-level natural language processing models for text analysis. It couples these modalities through feature-level fusion to enhance the performance of prediction. Extensive testing on public ally accessible datasets such as IEMOCAP and MELD has demonstrated the capacity of this model to identify emotions precisely and inferential mental problems such as anxiety and depression. The results show how multimodal systems can improve accuracy and the utility of AI powered tools within mental health monitoring instruments.