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Cognitive Measures in Developmental Language Disorder Classification in Monolingual and Bilingual Children: A Machine

Jade Plym1,2, Federico Màlato3, Pekka Lahti-Nuuttila1,2

  • 1Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland.

Journal of Speech, Language, and Hearing Research : JSLHR
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Machine learning models accurately identified developmental language disorder (DLD) in monolingual and bilingual children using a cognitive assessment battery. However, models trained on monolingual data performed poorly on bilingual children, highlighting differences in language development.

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Area of Science:

  • Child Language Acquisition
  • Developmental Psychology
  • Computational Linguistics

Background:

  • Developmental Language Disorder (DLD) affects language skills in children.
  • Differentiating DLD from typical development (TD) in monolingual and bilingual children presents unique challenges.
  • Machine learning (ML) offers potential for objective classification of DLD.

Purpose of the Study:

  • To evaluate the accuracy of a cognitive assessment battery using ML to distinguish DLD from TD in monolingual and bilingual children.
  • To assess the transferability of ML models trained on monolingual data to classify bilingual children.
  • To determine the relative importance of linguistic and nonlinguistic tasks in DLD classification.

Main Methods:

  • Utilized a random forest ML classification model.
  • Analyzed data from 4- to 7-year-old monolingual and sequential bilingual children with DLD (n=167) or TD (n=127).
  • Employed a cognitive assessment battery measuring language and cognitive domains.

Main Results:

  • The cognitive assessment battery achieved high accuracy for DLD/TD classification in monolinguals (91.3%) and good accuracy in bilinguals (84.7%).
  • An ML model trained on monolingual data showed lower accuracy (66.0%) when applied to bilingual children.
  • Language processing and verbal reasoning tasks were most crucial for classification; nonlinguistic tasks offered minimal improvement.

Conclusions:

  • ML models can effectively classify DLD in both monolingual and bilingual children using specific cognitive assessments.
  • Direct comparison of bilingual children's performance to monolingual standards is inappropriate.
  • Further research is needed to clarify the role of nonlinguistic functions in DLD detection.