Summary of Developing An End-to-end Framework For Predicting the Social Communication Severity Scores Of Children with Autism Spectrum Disorder, by Jihyun Mun et al.
Developing an End-to-End Framework for Predicting the Social Communication Severity Scores of Children with Autism Spectrum Disorder
by Jihyun Mun, Sunhee Kim, Minhwa Chung
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper proposes an end-to-end framework for automatically predicting the social communication severity of children with Autism Spectrum Disorder (ASD) from raw speech data. The framework uses automatic speech recognition and fine-tuned pre-trained language models to generate a final prediction score, which achieved a Pearson Correlation Coefficient of 0.6566 with human-rated scores. This method has the potential to be an accessible and objective tool for assessing ASD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to diagnose Autism Spectrum Disorder (ASD) in children more accurately. Right now, doctors rely on standardized tests that aren’t perfect. The researchers want to create a new way to do this using just what the child says. They used special machines to listen to kids with ASD and train other machines to understand what they’re saying. This new method is like a test that can be used by anyone, anywhere, which could help doctors diagnose ASD earlier and start helping kids get the help they need sooner. |
