{'fraction_non_alphanumeric': 0.02608047690014903, 'fraction_numerical': 0.0, 'mean_word_length': 5.958549222797927, '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}}
Discriminative Training for direct minimization of deletion, insertion and substitution errors

In this paper, we follow the minimum error principle for acoustic modeling and formulate error objectives in insertion, deletion, and substitution separately for minimization during training. This new training paradigm generalized from the MVE criterion can explain the direct relationship between recognition errors and detection errors by re-interpreting deletion, insertion, and substitution errors as miss, false alarm, and miss/false-alarm errors happening together. Under the MVE criterion, by applying two mis-verification measures for miss and false alarm errors selectively along with the types of recognition error definition, we developed three individual objective training criteria, minimum deletion error (MDE), minimum insertion error (MIE), and minimum substitution error (MSE), of which each objective function can directly minimize each of the three types of the recognition errors. In the TIMIT phone recognition task, the experimental results confirm that each objective criterion of MDE, MIE, and MSE results in primarily minimizing its target error type, respectively. Furthermore, a simple combination of the individual objective criteria outperforms the conventional string-based MCE in the overall recognition error rate.