Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Modeling in Therapy

Modeling in Therapy

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.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...

Autism Spectrum Disorder

Autism Spectrum Disorder

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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Long-term cross-variant Fc-mediated immune responses against SARS-CoV-2 induced by a heterologous adenoviral/inactivated virus prime-boost vaccination strategy.

NPJ vaccines·2026

Same author

Strengthening the global mental health workforce through collaborative education.

World psychiatry : official journal of the World Psychiatric Association (WPA)·2026

Same author

Transcriptomic analysis reveals functional compartmentalization of the digestive tract in Urechis unicinctus.

Genes & genomics·2026

Same author

Co-occurrence of rare variants implicates gene pairs in cytoskeletal pathways and is associated with increased severity in autism spectrum disorder.

Genome biology·2026

Same author

Exploring the Potential of a Scenario-Based Approach to Early Autism Spectrum Disorder Screening.

Psychiatry investigation·2026

Same author

Understanding the Mental Health in Parents of Children on the Autism Spectrum: Beyond Child-Driven Stressors.

Journal of autism and developmental disorders·2026

Search research articles

Related Experiment Video

Updated: Aug 15, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

End-to-End Model-Based Detection of Infants with Autism Spectrum Disorder Using a Pretrained Model.

Jung Hyuk Lee¹, Geon Woo Lee², Guiyoung Bong³

¹School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This study introduces an end-to-end neural network model for detecting autism spectrum disorder (ASD) in children's voices. The novel wav2vec2.0-based model significantly improved classification accuracy and recall compared to conventional methods.

Keywords:

autism spectrum disorder autoencoder bidirectional long short-term memory (BLSTM)end-to-end neural network joint optimization pretrained model

More Related Videos

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

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

Published on: October 3, 2018

Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

Published on: December 14, 2012

Related Experiment Videos

Last Updated: Aug 15, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

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

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

Published on: October 3, 2018

Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

Published on: December 14, 2012

Area of Science:

Artificial Intelligence
Speech Processing
Developmental Neuroscience

Background:

Autism spectrum disorder (ASD) detection often relies on explicit feature extraction, which can be limiting.
Developing objective, non-invasive methods for ASD identification is crucial for early intervention.

Purpose of the Study:

To propose and evaluate an end-to-end (E2E) neural network model for detecting autism spectrum disorder (ASD) in children's voices.
To compare the performance of a novel wav2vec2.0-based E2E model against conventional methods using autoencoder-based bidirectional long short-term memory (BLSTM) and eGeMAPS features.

Main Methods:

Developed an E2E neural network combining two feature extractors: bottleneck features from an autoencoder (eGeMAPS input) and context vectors from a pretrained wav2vec2.0 model (waveform input).
Employed a BLSTM-based classifier for ASD/TD (typical development) classification.
Optimized E2E models through fine-tuning and joint optimization strategies.
Evaluated models on two distinct datasets of children's voices.

Main Results:

The proposed wav2vec2.0-based E2E model with joint optimization demonstrated significant improvements.
Accuracy increased from 64.74% to 71.66%.
Unweighted average recall improved from 65.04% to 70.81% compared to the conventional model.

Conclusions:

The wav2vec2.0-based E2E model offers a more effective approach for detecting ASD from children's voices.
Joint optimization of the E2E model enhances classification performance.
This method avoids explicit feature engineering, simplifying the detection process.