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Related Concept Videos

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.
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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
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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.
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Social Anxiety Disorder01:28

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Social anxiety disorder, also known as social phobia, is characterized by an intense fear of social situations where one might face humiliation, rejection, embarrassment, or negative evaluation. This disorder leads individuals to avoid activities like casual conversations, public speaking, or seemingly simple tasks such as eating, signing documents, or swimming, in public settings. Its impact extends beyond discomfort, often significantly interfering with daily functioning and quality of life.
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Related Experiment Video

Updated: Jul 24, 2025

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
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Voice acoustics allow classifying autism spectrum disorder with high accuracy.

Frédéric Briend1, Céline David1, Silvia Silleresi2

  • 1UMR 1253, iBrain, Université de Tours, INSERM, 37000, Tours, France.

Translational Psychiatry
|July 8, 2023
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Summary

Objective voice analysis aids early autism spectrum disorder (ASD) detection. Acoustic features accurately identified children with ASD, offering a potential new diagnostic tool.

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Area of Science:

  • Neuroscience
  • Speech and Hearing Sciences
  • Developmental Pediatrics

Background:

  • Early identification of autism spectrum disorder (ASD) is critical for effective intervention.
  • Current diagnostic tools for ASD often lack sufficient diagnostic power.
  • Objective measures are needed to improve the accuracy and efficiency of autism detection.

Purpose of the Study:

  • To evaluate the classification performance of voice acoustic features for identifying children with ASD.
  • To compare the diagnostic accuracy of vocal biomarkers against neurotypical children and a heterogeneous group with developmental language disorder or sensorineural hearing loss with cochlear implants.

Main Methods:

  • A retrospective diagnostic study involving 108 children (38 ASD, 24 typically developing, 46 with DLD/CI).
  • Analysis of acoustic properties of speech samples from a nonword repetition task.
  • Development of a classification model using Monte Carlo cross-validation and ROC-supervised k-Means clustering.

Main Results:

  • Voice acoustics achieved 91% accuracy in classifying ASD versus typically developing children.
  • An 85% accuracy was observed when differentiating ASD from a heterogeneous group of non-autistic children.
  • The study demonstrated higher accuracy compared to previous research using similar methodologies.

Conclusions:

  • Voice acoustic parameters are a viable, easy-to-measure tool for aiding in the diagnosis of ASD.
  • Objective vocal biomarkers show significant potential for improving early autism detection.
  • These findings support the integration of acoustic voice analysis into diagnostic protocols for ASD.