ORIGINAL RESEARCH article
Front. Digit. Health
Sec. Health Informatics
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1274675
This article is part of the Research TopicArtificial Intelligence for Child Health and WellbeingView all 4 articles
The Noor Project: Fair Transformer Transfer Learning for Autism Spectrum Disorder Recognition from Speech
Provisionally accepted- 1Imperial College London, London, United Kingdom
- 2University of Augsburg, Augsburg, Bavaria, Germany
- 3Université Grenoble Alpes, Saint Martin d'Hères, Auvergne-Rhone-Alpes, France
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Early detection is crucial for managing incurable disorders, particularly autism spectrum disorder (ASD). Unfortunately, a considerable number of individuals with ASD receive a late diagnosis or remain undiagnosed. Speech holds a critical role in ASD, as a significant number of affected individuals experience speech impairments or remain non-verbal. To address this, we use speech analysis for automatic ASD recognition in children by classifying their speech as either autistic or typically developing. However, due to the lack of large labelled datasets, we leverage two smaller datasets to explore deep transfer learning methods. We investigate two fine-tuning approaches:(1) Discriminative Fine-Tuning (D-FT), which is pre-trained on a related dataset before being tuned on a similar task, and (2) Wav2Vec 2.0 Fine-Tuning (W2V2-FT), which leverages self-supervised speech representations pre-trained on a larger, unrelated dataset. We perform two distinct classification tasks: (a) a binary task to determine typicality, classifying speech as either that of a typically developing (TD) child or an atypically developing (AD) child; and (b) a four-class diagnosis task, which further classifies atypical cases into ASD, dysphasia (DYS), or pervasive developmental disorder-not otherwise specified (NOS), alongside TD. This research aims to improve early recognition strategies, particularly for individuals with ASD. The findings suggest that transfer learning methods can be a valuable tool for autism recognition from speech. For the typicality classification task (TD vs. AD), the D-FT model achieved the highest test UAR (94.8%), outperforming W2V2-FT (91.5%). In the diagnosis task (TD, ASD, DYS, NOS), D-FT also demonstrated superior performance (60.9% UAR) compared to W2V2-FT (54.3%). These results highlight the potential of transfer learning for speech-based ASD recognition and underscore the challenges of multi-class classification with limited labeled data.
Keywords: Autism Spectrum Disorder, Child speech, artificial intelligence, deep learning, transformer, Transfer Learning, Fairness in AI
Received: 08 Aug 2023; Accepted: 28 May 2025.
Copyright: © 2025 Al Futaisi, Schuller, Ringeval and Pantic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Najla Al Futaisi, Imperial College London, London, United Kingdom
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