Summary of Medical Speech Symptoms Classification Via Disentangled Representation, by Jianzong Wang et al.
Medical Speech Symptoms Classification via Disentangled Representation
by Jianzong Wang, Pengcheng Li, Xulong Zhang, Ning Cheng, Jing Xiao
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 DRSC model is designed to automatically classify spoken language related to medical symptoms by disentangling intent and content representations from textual-acoustic data. The model uses intent encoders to extract representations from both text and Mel-spectrogram domains, which are then combined into a joint representation fed into a decision layer for classification. Experimental results demonstrate an average accuracy rate of 95% in detecting 25 different medical symptoms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve the understanding of spoken language related to medical symptoms. The proposed model can automatically classify spoken language and identify specific symptoms, which is important for diagnosing medical conditions. The model uses a combination of text and audio features to identify intent and content in spoken language. |
Keywords
» Artificial intelligence » Classification
