Evaluating the effect of predicting oral reading miscues

This paper extends and evaluates previously published meth- ods 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 com- bined 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 predic- tion scheme.