Summary of Modality-order Matters! a Novel Hierarchical Feature Fusion Method For Cosam: a Code-switched Autism Corpus, by Mohd Mujtaba Akhtar et al.
Modality-Order Matters! A Novel Hierarchical Feature Fusion Method for CoSAm: A Code-Switched Autism Corpus
by Mohd Mujtaba Akhtar, Girish, Muskaan Singh, Orchid Chetia Phukan
First submitted to arxiv on: 19 Jul 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 The proposed study aims to enhance the early detection of Autism Spectrum Disorder (ASD) in children through the analysis of code-switched speech (English and Hindi). A novel hierarchical feature fusion method is introduced, which integrates acoustic, paralinguistic, and linguistic information using Transformer Encoders. The approach involves collecting a dataset of code-switched speech recordings from children diagnosed with ASD and a matched control group. The study uses MFCCs and extensive statistical attributes to capture speech pattern variability and complexity, achieving an accuracy of 98.75% using a combination of acoustic and linguistic features followed by paralinguistic features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers aim to improve the early detection of Autism Spectrum Disorder (ASD) in children. They create a new way to analyze code-switched speech (English and Hindi). This method combines different types of information from the speech, like sound, tone, and words. The team collects recordings from children with ASD and normal children. Then, they use special computer tools to analyze these recordings and look for patterns that might help them detect ASD earlier. |
Keywords
* Artificial intelligence * Transformer
