Pronunciation verification of children’s speech for automatic literacy assessment

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 recognition- and 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