The Noor Project: fair transformer transfer learning for autism spectrum disorder recognition from speech
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Digital Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1274675/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849736952065032192 |
|---|---|
| author | Najla D. Al Futaisi Björn W. Schuller Björn W. Schuller Fabien Ringeval Maja Pantic |
| author_facet | Najla D. Al Futaisi Björn W. Schuller Björn W. Schuller Fabien Ringeval Maja Pantic |
| author_sort | Najla D. Al Futaisi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-535bbaf2e951459cbec96056985935af |
| institution | DOAJ |
| issn | 2673-253X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj-art-535bbaf2e951459cbec96056985935af2025-08-20T03:07:06ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2025-08-01710.3389/fdgth.2025.12746751274675The Noor Project: fair transformer transfer learning for autism spectrum disorder recognition from speechNajla D. Al Futaisi0Björn W. Schuller1Björn W. Schuller2Fabien Ringeval3Maja Pantic4GLAM – Group on Language, Audio & Music, Imperial College London, London, United KingdomGLAM – Group on Language, Audio & Music, Imperial College London, London, United KingdomChair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, GermanyGrenoble INP, LIG, University Grenoble Alpes, Inria, CNRS, Grenoble, FranceiBUG – Intelligent Behaviour Understanding Group, Imperial College London, London, United KingdomEarly 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.https://www.frontiersin.org/articles/10.3389/fdgth.2025.1274675/fullautism spectrum disorderchild speechartificial intelligencedeep learningtransformertransfer learning |
| spellingShingle | Najla D. Al Futaisi Björn W. Schuller Björn W. Schuller Fabien Ringeval Maja Pantic The Noor Project: fair transformer transfer learning for autism spectrum disorder recognition from speech Frontiers in Digital Health autism spectrum disorder child speech artificial intelligence deep learning transformer transfer learning |
| title | The Noor Project: fair transformer transfer learning for autism spectrum disorder recognition from speech |
| title_full | The Noor Project: fair transformer transfer learning for autism spectrum disorder recognition from speech |
| title_fullStr | The Noor Project: fair transformer transfer learning for autism spectrum disorder recognition from speech |
| title_full_unstemmed | The Noor Project: fair transformer transfer learning for autism spectrum disorder recognition from speech |
| title_short | The Noor Project: fair transformer transfer learning for autism spectrum disorder recognition from speech |
| title_sort | noor project fair transformer transfer learning for autism spectrum disorder recognition from speech |
| topic | autism spectrum disorder child speech artificial intelligence deep learning transformer transfer learning |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1274675/full |
| work_keys_str_mv | AT najladalfutaisi thenoorprojectfairtransformertransferlearningforautismspectrumdisorderrecognitionfromspeech AT bjornwschuller thenoorprojectfairtransformertransferlearningforautismspectrumdisorderrecognitionfromspeech AT bjornwschuller thenoorprojectfairtransformertransferlearningforautismspectrumdisorderrecognitionfromspeech AT fabienringeval thenoorprojectfairtransformertransferlearningforautismspectrumdisorderrecognitionfromspeech AT majapantic thenoorprojectfairtransformertransferlearningforautismspectrumdisorderrecognitionfromspeech AT najladalfutaisi noorprojectfairtransformertransferlearningforautismspectrumdisorderrecognitionfromspeech AT bjornwschuller noorprojectfairtransformertransferlearningforautismspectrumdisorderrecognitionfromspeech AT bjornwschuller noorprojectfairtransformertransferlearningforautismspectrumdisorderrecognitionfromspeech AT fabienringeval noorprojectfairtransformertransferlearningforautismspectrumdisorderrecognitionfromspeech AT majapantic noorprojectfairtransformertransferlearningforautismspectrumdisorderrecognitionfromspeech |