{'fraction_non_alphanumeric': 0.021174205967276226, 'fraction_numerical': 0.008662175168431183, 'mean_word_length': 5.227544910179641, '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}}
Investigating automatic recognition of non-native children's speech

This paper presents an initial effort in the area of nonnative children’s speech recognition by exploiting two children databases, one consisting of speech collected from native English children, the other one consisting of English sentences read by Italian learners of English in the same age range of the native speakers. First, a baseline speech recognizer for British English was trained on the corpus of native speech and applied to recognize native and non-native speech. Word error rates achieved for Italian children were 100%-600% higher than those achieved for native English children of the same age. By using a small amount of non-native speech from a group of Italian learners of English, acoustic models were adapted to this particular category of speakers. Adaptation of both contextindependent and context-dependent HMMs showed to greatly improve recognition performance on non-native speech, obtaining relative reductions in word error rate up to 66.2%.