Automated Vocal Analysis of Children With Hearing Loss and Their Typical and Atypical Peers

Abstract

In Brief Objectives: This study investigated automatic assessment of vocal development in children with hearing loss compared with children who are typically developing, have language delays, and have autism spectrum disorder. Statistical models are examined for performance in a classification model and to predict age within the four groups of children. Design: The vocal analysis system analyzed 1913 whole-day, naturalistic acoustic recordings from 273 toddlers and preschoolers comprising children who were typically developing, hard of hearing, language delayed, or autistic. Results: Samples from children who were hard of hearing patterned more similarly to those of typically developing children than to the language delayed or autistic samples. The statistical models were able to classify children from the four groups examined and estimate developmental age based on automated vocal analysis. Conclusions: This work shows a broad similarity between children with hearing loss and typically developing children, although children with hearing loss show some delay in their production of speech. Automatic acoustic analysis can now be used to quantitatively compare vocal development in children with and without speech-related disorders. The work may serve to better distinguish among various developmental disorders and ultimately contribute to improved intervention. Automatic analyses of a large dataset of naturalistic child vocalizations were performed on recordings from children who were typically developing, were hard of hearing, were language delayed, and had autism spectrum disorder. Based on the automatic audio processing, the statistical models were able to accurately classify children and predict child age, taking group classification into account. Children who were hard of hearing patterned more similarly to the typically developing group of children than the language-delayed children or children with autism. The work may serve to better distinguish among various developmental disorders and ultimately contribute to improved intervention.