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Related Experiment Video

Updated: Sep 4, 2025

Examining Bilingual Language Control Using the Stroop Task

Examining Bilingual Language Control Using the Stroop Task

Published on: February 26, 2020

Identifying Language Disorder in Bilingual Children Using Automatic Speech Recognition.

Nahar Albudoor¹, Elizabeth D Peña²

¹Gallaudet University, Washington, DC.

Journal of Speech, Language, and Hearing Research : JSLHR

|July 20, 2022

Summary

This summary is machine-generated.

Automatic speech recognition (ASR) shows promise for identifying developmental language disorder (DLD) in bilingual children. This technology offers a feasible approach to dual-language assessment, potentially improving diagnostic accuracy.

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Area of Science:

Linguistics
Speech-Language Pathology
Computational Linguistics

Background:

Diagnosing developmental language disorder (DLD) in bilingual children is challenging due to varied language exposure.
Dual-language testing is accurate but resource-intensive.
Automatic Speech Recognition (ASR) offers a potential solution for efficient bilingual assessment.

Purpose of the Study:

To evaluate the feasibility of using dual-language ASR for identifying DLD in bilingual children.
To compare ASR-based scoring with human scoring for DLD assessment.
To determine the accuracy of ASR in classifying DLD in Spanish-English bilinguals.

Main Methods:

Eighty-four Spanish-English bilingual second graders (25 with DLD, 59 without) completed a morphosyntax assessment in both languages.
Responses were scored manually by examiners and programmatically by an ASR application.
A commercial speech-to-text algorithm was employed within the ASR system.

Main Results:

Moderate agreement (k = .54) was found between human and ASR scoring.
Classification accuracy for DLD was comparable: human (92%) and ASR (88%).
ASR classification accuracy improved to 94% after removing less discriminating items.

Conclusions:

ASR demonstrates technical feasibility as a tool for bilingual expressive language assessment.
This technology could help overcome resource limitations in diagnosing DLD in bilingual children.
Further research is warranted to refine ASR for clinical use in diverse populations.