Identifying Language Disorder in Bilingual Children Using Automatic Speech Recognition

Abstract

Purpose: The differential diagnosis of developmental language disorder (DLD) in bilingual children represents a unique challenge due to their distributed language exposure and knowledge. The current evidence indicates that dual-language testing yields the most accurate classification of DLD among bilinguals, but there are limited personnel and resources to support this practice. The purpose of this study was therefore to determine the feasibility of dual-language automatic speech recognition (ASR) for identifying DLD in bilingual children. Method: Eighty-four Spanish–English bilingual second graders with ( n = 25) and without ( n = 59) confirmed diagnoses of DLD completed the Bilingual English-Spanish Assessment–Middle Extension Morphosyntax in both languages. Their responses on a subset of items were scored manually by human examiners and programmatically by a researcher-developed ASR application employing a commercial speech-to-text algorithm. Results: Results demonstrated moderate overall item-by-item scoring agreement ( k = .54) and similar classification accuracy values (human = 92%, ASR = 88%) between the two methods using the best-language score. Classification accuracy of the ASR method increased to 94% of cases correctly classified when test items with poorer discrimination in the ASR condition were eliminated. Conclusion: This study provides preliminary support for the technical feasibility of ASR as a bilingual expressive language assessment tool. Supplemental Material: https://doi.org/10.23641/asha.20249994