Pronunciation verification of children²s speech for automatic literacy assessment

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 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.