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Vocal markers of autism: Assessing the generalizability of machine learning models

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  • Astrid Rybner, Aarhus University
  • ,
  • Emil Trenckner Jessen, Aarhus University
  • ,
  • Marie Damsgaard Mortensen, Aarhus University
  • ,
  • Stine Nyhus Larsen
  • ,
  • Ruth Grossman, Emerson College
  • ,
  • Niels Bilenberg, University of Southern Denmark
  • ,
  • Cathriona Cantio, University of Southern Denmark
  • ,
  • Jens Richardt Møllegaard Jepsen, Capitol Region of Denmark
  • ,
  • Ethan Weed
  • Arndis Simonsen
  • Riccardo Fusaroli

Machine learning (ML) approaches show increasing promise in their ability to identify vocal markers of autism. Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected, for example, using a different speech task or in a different language. In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. We train promising published ML models of vocal markers of autism on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on (i) different participants from the same study, performing the same task; (ii) the same participants, performing a different (but similar) task; (iii) a different study with participants speaking a different language, performing the same type of task. While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to different, though similar, tasks and not at all to new languages. The ML pipeline is openly shared. Generalizability of ML models of vocal markers of autism is an issue. We outline three recommendations for strategies researchers could take to be more explicit about generalizability and improve it in future studies. Lay Summary: Machine learning approaches promise to be able to identify autism from voice only. These models underestimate how diverse the contexts in which we speak are, how diverse the languages used are and how diverse autistic voices are. Machine learning approaches need to be more careful in defining their limits and generalizability.

Original languageEnglish
JournalAutism Research
Pages (from-to)1018-1030
Number of pages13
Publication statusPublished - Jun 2022

Bibliographical note

Publisher Copyright:
© 2022 International Society for Autism Research and Wiley Periodicals LLC.

    Research areas

  • autism spectrum disorder, biobehavioral markers, generalizability, machine learning, voice

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