{'fraction_non_alphanumeric': 0.0103397341211226, 'fraction_numerical': 0.0, 'mean_word_length': 5.78, '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}}
Lattice segmentation and support vector machines for large vocabulary continuous speech recognition

Lattice segmentation procedures are used to spot possible recognition errors in first-pass recognition hypotheses produced by a large vocabulary continuous speech recognition system. This approach is analyzed in terms of its ability to reliably identify, and provide good alternatives for, incorrectly hypothesized words. A procedure is described to train and apply support vector machines to strengthen the first pass system where it was found to be weak, resulting in small but statistically significant recognition improvements on a large test set of conversational speech.