{'fraction_non_alphanumeric': 0.02461139896373057, 'fraction_numerical': 0.0012953367875647669, 'mean_word_length': 5.134920634920635, '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}}
Minimum Bayes risk discriminative language models for Arabic speech recognition

In this paper we explore discriminative language modeling (DLM) on highly optimized state-of-the-art large vocabulary Arabic broadcast speech recognition systems used for the Phase 5 DARPA GALE Evaluation. In particular, we study in detail a minimum Bayes risk (MBR) criterion for DLM. MBR training outperforms perceptron training. Interestingly, we found that our DLMs generalized to mismatched conditions, such as using a different acoustic model during testing. We also examine the interesting problem of unsupervised DLM training using a Bayes risk metric as a surrogate for word error rate (WER). In some experiments, we were able to obtain about half of the gain of the supervised DLM.