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Related Concept Videos

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
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Related Experiment Video

Updated: Feb 20, 2026

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism
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Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

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COMPUTATIONAL ANALYSIS OF TRAJECTORIES OF LINGUISTIC DEVELOPMENT IN AUTISM.

Emily Prud'hommeaux1, Eric Morley2, Masoud Rouhizadeh2

  • 1Center for Language Sciences, University of Rochester.

SLT ... : ... IEEE Workshop on Spoken Language Technology : Proceedings. IEEE Workshop on Spoken Language Technology
|October 24, 2017
PubMed
Summary

This study analyzed language in children with and without autism, finding that while many differences persist with age, pragmatic skills like staying on topic improve. This highlights the importance of age in autism language research.

Keywords:
automated neurological assessmentclinical spoken language analysisnatural language processing

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Area of Science:

  • Linguistics
  • Developmental Psychology
  • Computational Linguistics

Background:

  • Autism Spectrum Disorder (ASD) is characterized by semantic and pragmatic language deficits.
  • Automated linguistic analysis shows promise for studying autism language.
  • Previous research often focused on young children or broad age ranges.

Purpose of the Study:

  • To investigate age-related changes in linguistic features of autism.
  • To compare language characteristics in children with and without autism across specific age bands.
  • To identify age-constant versus age-variable linguistic markers in autism.

Main Methods:

  • Extraction of numerous linguistic features from narrative speech samples.
  • Analysis of data from two distinct age groups of children with and without autism.
  • Comparison of computational linguistic measures between diagnostic groups.

Main Results:

  • Significant differences in semantic and pragmatic expression were observed between children with and without autism.
  • Certain pragmatic measures, such as topic maintenance and reduced digressions, showed improvement with age in children with autism.
  • Many linguistic differences remained consistent across the studied age ranges.

Conclusions:

  • Automated linguistic analysis is valuable for characterizing autism language.
  • Pragmatic language skills in autism may show developmental improvements.
  • Age range is a critical factor in the interpretation of computational language analysis in autism research.