FACTORS THAT INFLUENCE EFFICACY OF AUTOMATED LANGUAGE SAMPLE ANALYSIS FOR CHILDREN WITH DEVELOPMENTAL LANGUAGE DISORDER

Abstract

Automated language sample analysis (LSA) offers clinicians the ability to reduce time and labor loads often associated with the manual task process. Children with DLD are one of the main populations LSA benefits, due to individualized language deficit profiles of this population. To provide clinicians with straightforward standards to increase the accuracy and clinical utility of automated LSA for children with DLD, we investigated what factors at the language sample retrieval, transcription, and analysis stages influence accuracy. Our study specifically explored what acoustic-related factors during sample retrieval may influence transcription, and what metrics during analysis may influence result interpretations. Automated LSA for children with DLD (n=44) performed with greater accuracy once children had received language treatment for 10 weeks. Speech intelligibility ratings of children influenced automated transcription consistently, while background noise and audio recording equipment quality were not found to significantly influence transcription accuracy. At the metric analysis stage, automated transcription metric analysis did not track morphological and lexical gains to the same significance as manual transcription analysis did. Automated metric analysis with metrics that can only feasibly be computed by voice activity detection did find statistically significant gains between pre- and post- treatment transcriptions. If opting to use automated LSA, clinicians should consider speech intelligibility of children and metrics used in analysis, as these factors seem to impact accuracy of LSA as a tool for designing and assessing treatment for children with DLD.