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

TL;DR

This paper introduces an automated end-to-end framework that predicts social communication severity in children with ASD from raw speech data, aiming to provide an objective assessment tool.

Contribution

It develops a novel framework combining speech recognition and language models specifically fine-tuned for children with ASD to predict severity scores.

Findings

Achieved a Pearson correlation of 0.6566 with human scores.

Demonstrated potential for an accessible, objective ASD assessment tool.

Integrates speech recognition with language modeling for severity prediction.

Abstract

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.