Abstract
Children need to master reading letter-names and letter-sounds before reading phrases and sentences. Pronunciation assessment of letter-names and letter-sounds read aloud is an important component of preliterate children's education, and automating this process can have several advantages. The goal of this work was to automatically verify letter-names spoken by kindergarteners and first graders in realistic classroom noise conditions.. We applied the same techniques developed in our previous work on automatic letter-sound verification by comparing and optimizing different acoustic models, dictionaries, and decoding grammars. Our final system was unbiased with respect to the child's grade, age, and native language and achieved 93.1% agreement (0.813 kappa agreement) with human evaluators, who agreed among themselves 95.4% of the time (0.891 kappa).