Summary of Integrating Large Language Models Into a Tri-modal Architecture For Automated Depression Classification on the Daic-woz, by Santosh V. Patapati
Integrating Large Language Models into a Tri-Modal Architecture for Automated Depression Classification on the DAIC-WOZ
by Santosh V. Patapati
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed BiLSTM-based tri-modal model-level fusion architecture uses Mel Frequency Cepstral Coefficients, Facial Action Units, and a GPT-4 model to process text data for binary classification of depression from clinical interview recordings. This novel approach achieves impressive results on the DAIC-WOZ AVEC 2016 Challenge, surpassing all baseline models and multiple state-of-the-art models. The architecture’s performance is evaluated using metrics such as accuracy, F1-Score, precision, and recall. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Major Depressive Disorder affects 300 million people worldwide. This paper presents a new way to detect depression from recordings of clinical interviews. It combines three types of data: sounds, facial expressions, and text. The model uses large language models to process the text data. This is the first time this approach has been used for this task. The results are impressive, beating all other models tested. |
Keywords
» Artificial intelligence » Classification » F1 score » Gpt » Precision » Recall
