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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: Feb 22, 2026

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Published on: January 23, 2017

Methods for eliciting, annotating, and analyzing databases for child speech development.

Mary E Beckman¹, Andrew R Plummer², Benjamin Munson³

¹Linguistics, Ohio State University.

Computer Speech & Language

|September 26, 2017

Summary

This summary is machine-generated.

Automatic speech recognition (ASR) models struggle with child speech due to developmental differences. Research is needed for age-appropriate computational models of child speech acquisition.

Keywords:

automatic speech recognition big data corpora child speech development phonetic transcription spectral kinematics

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Area of Science:

Speech Science
Computational Linguistics
Developmental Psychology

Background:

Automatic speech recognition (ASR) methods have greatly advanced adult speech analysis.
Current ASR models, developed for adults, are inadequate for analyzing young children's speech data.

Purpose of the Study:

To identify challenges in applying ASR to child speech databases.
To propose solutions for developing and utilizing child speech resources.

Main Methods:

Analysis of differences between adult and child speech impacting ASR.
Investigation of annotation schemas and analysis techniques for child speech variability.

Main Results:

Children's smaller, developing vocal tracts cause significant between-talker variability.
Developing speech motor control, vocabulary, and phonology increase within-talker variability.
Adult-centered schemas fail to capture unique child speech phenomena like covert contrasts.

Conclusions:

ASR models for child speech require understanding unique developmental differences.
New annotation and analysis methods are needed to capture child speech variability.
Further research into the developing articulatory, lexical, and phonological systems is crucial for age-appropriate computational models.