  Major Depressive Disorder (MDD) is a pervasive mental health condition that
affects 300 million people worldwide. This work presents a novel, BiLSTM-based
tri-modal model-level fusion architecture for the binary classification of
depression from clinical interview recordings. The proposed architecture
incorporates Mel Frequency Cepstral Coefficients, Facial Action Units, and uses
a two-shot learning based GPT-4 model to process text data. This is the first
work to incorporate large language models into a multi-modal architecture for
this task. It achieves impressive results on the DAIC-WOZ AVEC 2016 Challenge
cross-validation split and Leave-One-Subject-Out cross-validation split,
surpassing all baseline models and multiple state-of-the-art models. In
Leave-One-Subject-Out testing, it achieves an accuracy of 91.01%, an F1-Score
of 85.95%, a precision of 80%, and a recall of 92.86%.
