# Developing an End-to-End Framework for Predicting the Social Communication Severity Scores of Children with Autism Spectrum Disorder

arXiv: https://arxiv.org/abs/2409.00158 · HTML: https://mlsanity.com/paper/2409.00158/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [KLUE: Korean Language Understanding Evaluation](https://mlsanity.com/paper/2105.09680/) (2021)
- [SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks](https://mlsanity.com/paper/2303.00733/) (2023)
- [KR-BERT: A Small-Scale Korean-Specific Language Model](https://mlsanity.com/paper/2008.03979/) (2020)
- [Prompt Tuning for Discriminative Pre-trained Language Models](https://mlsanity.com/paper/2205.11166/) (2022)
- [Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection](https://mlsanity.com/paper/2210.16539/) (2022)
- [A Metamodel Structure For Regression Analysis: Application To Prediction Of Autism Spectrum Disorder Severity](https://mlsanity.com/paper/2105.02874/) (2021)
- [Speech Corpus for Korean Children with Autism Spectrum Disorder: Towards Automatic Assessment Systems](https://mlsanity.com/paper/2402.15539/) (2024)

## Abstract (arXiv)

Autism Spectrum Disorder (ASD) is a lifelong condition that significantly influencing an individual's communication abilities and their social interactions. Early diagnosis and intervention are critical due to the profound impact of ASD's characteristic behaviors on foundational developmental stages. However, limitations of standardized diagnostic tools necessitate the development of objective and precise diagnostic methodologies. This paper proposes an end-to-end framework for automatically predicting the social communication severity of children with ASD from raw speech data. This framework incorporates an automatic speech recognition model, fine-tuned with speech data from children with ASD, followed by the application of fine-tuned pre-trained language models to generate a final prediction score. Achieving a Pearson Correlation Coefficient of 0.6566 with human-rated scores, the proposed method showcases its potential as an accessible and objective tool for the assessment of ASD.