Abstract
Arguably the most important part of automatically assessing a new reader’s literacy is in verifying his pronunciation of read-aloud target words. But the pronunciation evaluation task is especially difficult in children, non-native speakers, and pre-literates. Traditional likelihood ratio thresholding methods do not generalize easily, and even expert human evaluators do not always agree on what constitutes an acceptable pronunciation. We propose new recognitionand alignment-based features in a decision tree classification framework, along with the use of prior linguistic information and human perceptual evaluations. Our classification methods demonstrate a 91% agreement with the voted results of 20 human evaluators who agree among themselves 85% of the time.