Evaluating the effect of predicting oral reading miscues

This paper extends and evaluates previously published methods for predicting likely miscues in children’s oral reading in a Reading Tutor that listens. The goal is to improve the speech recognizer’s ability to detect miscues but limit the number of “false alarms ” (correctly read words misclassified as incorrect). The “rote ” method listens for specific miscues from a training corpus. The “extrapolative ” method generalizes to predict other miscues on other words. We construct and evaluate a scheme that combines our rote and extrapolative models. This combined approach reduced false alarms by 0.52 % absolute (12% relative) while simultaneously improving miscue detection by 1.04 % absolute (4.2 % relative) over our existing miscue prediction scheme. 1.