Integrating Large Language Models into a Tri-Modal Architecture for
  Automated Depression Classification
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%.
66a837833873fba1999297fc
Santosh V. Patapati
claimed_verified
2024-07-30T07:26:04.036Z
64ba972e90dfdda6ab7fbf54
https://cdn-avatars.huggingface.co/v1/production/uploads/64ba972e90dfdda6ab7fbf54/631OMPITxvxH6u4sGhoHa.jpeg
Santosh Patapati
user
Soontosh
Integrating Large Language Models into a Tri-Modal Architecture for
  Automated Depression Classification
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%.
A BiLSTM-based multi-modal model combines audio, facial, and text data using a GPT-4 model to classify depression, achieving state-of-the-art performance in binary classification tasks.
BiLSTM Mel Frequency Cepstral Coefficients Facial Action Units GPT-4 two-shot learning multi-modal architecture