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Using Computational Models to Detect Autistic Tendencies for Children from their Story Book Narratives

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Abstract

Diagnosing autism spectrum disorder (ASD) conventionally demands significant time and resources. Language deficits are key markers of ASD, particularly in constructing narratives. This study leverages computational models to analyze story book narratives from seven children with ASD and 16 typically-developing (TD) peers. By transcribing and training models on limited data using augmentation techniques, our best model achieved over 90% accuracy, sensitivity, and specificity-outperforming previous models by 20% in ASD detection. This research showcases the efficacy of our approach in efficiently assessing language abilities and identifying ASD tendencies. The method holds promise for enhancing diagnostic efficiency and providing comprehensive language evaluations to support children with ASD and their caregivers.

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Data Availability

The collected dataset can only be shared after the consent from the participants can be obtained and documented.

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Acknowledgements

We extend our heartfelt gratitude to the children and their families who took part in this study, generously contributing to our dataset. We are also grateful to the Zhulian Elementary School in East District of Hsinchu City and the Hsinchu Autism Association for their cooperation in recruiting the participants for the study and providing the experiment venue.

Funding

This research was funded by [109-2221-E-468-014-MY3] (awarded to Dr. Arbee L.P. Chen) from National Science and Technology Council, Republic of China.

Author information

Authors and Affiliations

  1. Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan

    Ruihan Sun

  2. Department of Special Education, National Tsing Hua University, Hsinchu, Taiwan

    Jasin Wong

  3. College of Education, National Tsing Hua University, Hsinchu, Taiwan

    Eva E. Chen

  4. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Arbee L. P. Chen

Authors
  1. Ruihan Sun
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  2. Jasin Wong
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  3. Eva E. Chen
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  4. Arbee L. P. Chen
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ruihan Sun. The first draft of the manuscript was written by Ruihan Sun and all other authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Arbee L. P. Chen.

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Competing Interests

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Ethical Approval

This study was reviewed and approved by the Institutional Review Board at China Medical University (IRB number: CRREC-112-002).

Consent to Participate

We obtained the written consent from the legal guardian of each participant.

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Sun, R., Wong, J., Chen, E.E. et al. Using Computational Models to Detect Autistic Tendencies for Children from their Story Book Narratives. Multimed Tools Appl (2025). https://doi.org/10.1007/s11042-025-20600-z

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  • DOI: https://doi.org/10.1007/s11042-025-20600-z

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