Abstract
Automatic literacy assessment is an area of research that has shown significant progress in recent years. Technology can be used to automatically administer reading tasks and analyze and interpret children's reading skills. It has the potential to transform the classroom dynamic by providing useful information to teachers in a repeatable, consistent, and affordable way. While most previous research has focused on automatically assessing children reading words and sentences, assessments of children's earlier foundational skills is needed. We address this problem in this research by automatically verifying preliterate children's pronunciations of English letter-names and the sounds each letter represents (“letter-sounds”). The children analyzed in this study were from a diverse bilingual background and were recorded in actual kindergarten to second grade classrooms. We first manually verified (accept/reject) the letter-name and letter-sound utterances, which serve as the ground-truth in this study. Next, we investigated four automatic verification methods that were based on automatic speech recognition techniques. We attained percent agreement with human evaluations of 90% and 85% for the letter-name and letter-sound tasks, respectively. Humans agree between themselves an average of 95% of the time for both tasks. We discuss the various confounding factors for this assessment task, such as background noise and the presence of disfluencies, that impact automatic verification performance.