Summary of Context-aware Deep Learning For Multi Modal Depression Detection, by Genevieve Lam et al.
Context-Aware Deep Learning for Multi Modal Depression Detection
by Genevieve Lam, Huang Dongyan, Weisi Lin
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper presents a novel approach to detecting depression from clinical interviews using multi-modal machine learning. The proposed method combines pre-trained Transformers with data augmentation for textual data and deep 1D convolutional neural networks (CNNs) for acoustic feature modeling. The approach outperforms state-of-the-art methods in both audio and text modalities, as well as the combined setting. The study utilizes the Distress Analysis Interview Corpus and demonstrates the effectiveness of multi-modal deep learning models for depression detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer programs to detect when someone is feeling sad or depressed after talking to a doctor. They used two different types of computer programs: one that looks at what people are saying, and another that listens to their voice. The computer programs worked really well, especially when they were used together. This could help doctors diagnose depression more accurately. |
Keywords
» Artificial intelligence » Data augmentation » Deep learning » Machine learning » Multi modal
