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Language Development

Language Development

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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Related Experiment Video

Updated: Sep 14, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Developing age-specific protocols for pediatric voice databases in artificial intelligence research.

Laurie Russell¹, Yael Bensoussan², Evan Ng³

¹Divisions of Communication Disorders & Otolaryngology - Head and Neck Surgery, The Hospital for Sick Children, Toronto, Canada.

International Journal of Pediatric Otorhinolaryngology

|July 18, 2025

Summary

This summary is machine-generated.

New age-specific voice analysis protocols were developed for children to improve data collection for artificial intelligence research. These standardized methods ensure reliable acoustic measurements for advancing pediatric clinical sciences.

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Area of Science:

Pediatric otolaryngology
Speech-language pathology
Artificial intelligence in clinical sciences

Background:

Children's voice and communication skills change with age, requiring tailored analysis methods.
Current lack of standardized pediatric voice protocols hinders reliable data collection and AI research.
Developing age-specific protocols is crucial for accurate acoustic measurements in children.

Purpose of the Study:

To develop and validate age-specific voice data collection protocols for children.
To create a standardized pediatric voice database for AI-driven research.
To enhance the accuracy and reliability of voice analysis in pediatric populations.

Main Methods:

Iterative protocol development over six months by a multidisciplinary team.
Comprehensive literature review and focus on high-quality, consistent data for AI modeling.
Protocols designed for specific age groups (2-4, 4-6, 6-10, 10+ years) using an iPad application and headset microphones.

Main Results:

Four age-specific protocols were created, targeting key acoustic parameters relevant to each developmental stage.
Protocols were successfully administered to 100 pediatric patients (ages 2-18) in a clinical setting.
Standardized data collection was achieved using child-friendly methods and technology.

Conclusions:

Developed age-specific protocols standardize pediatric voice and acoustic data collection.
This standardization will serve as a valuable resource for advancing AI applications in clinical sciences.
Future validation will confirm the feasibility and reliability of these protocols for AI-driven innovations.