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Related Concept Videos

Language Development01:22

Language Development

284
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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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.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Learning Disabilities01:25

Learning Disabilities

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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
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Related Experiment Video

Updated: May 13, 2025

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Automated Approaches to Screening Developmental Language Disorder: A Comprehensive Review and Future Prospects.

Yangna Hu1, Cindy Sing Bik Ngai1, Sihui Chen1

  • 1The Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon.

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Summary

This review of automatic screening for developmental language disorder (DLD) found models use various languages and features. Future research needs larger datasets and improved sensitivity for DLD detection.

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Area of Science:

  • Speech-language pathology
  • Computational linguistics
  • Machine learning in healthcare

Background:

  • Developmental Language Disorder (DLD) is a neurodevelopmental deficit impacting language.
  • Biomedical etiologies for DLD are not yet established.
  • Automated screening methods offer potential for early DLD identification.

Purpose of the Study:

  • To systematically review automatic screening methods for DLD.
  • To analyze languages, datasets, features, and classification techniques used.
  • To identify strengths, weaknesses, and future research directions.

Main Methods:

  • Systematic literature review of studies published before March 2024.
  • Searches conducted across PubMed, Web of Science, Scopus, and PsycINFO.
  • Inclusion criteria focused on automated DLD screening systems for children.

Main Results:

  • 23 studies were reviewed, focusing on Czech, Italian, Mandarin, Spanish, and English.
  • Commonly used features include acoustic, textural, speech, and non-speech data.
  • Classification methods range from traditional machine learning to deep learning (CNNs, LSTMs).
  • Need for larger, multilingual datasets and enhanced system sensitivity was identified.

Conclusions:

  • Significant advancements in automatic DLD screening have been made.
  • Future research should focus on integrating diverse features and algorithms.
  • Addressing variations in age, gender, severity, and comorbidities is crucial for improved DLD screening.