{'fraction_non_alphanumeric': 0.01880141010575793, 'fraction_numerical': 0.007050528789659225, 'mean_word_length': 5.816, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Pronunciation verification of children²s 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 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.