Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models
Table 1: WERs on the test set after minimum WER training for uni- and bi-directional LAS models. The proposed procedure improves WER by up to 8.2% relative to the CE-trained baseline system.
['System', 'WER(%)', 'Rescored WER(%)']
We report results after directly decoding the models to produce grapheme sequences using a beam-search decoding with 8 beams (column 2) as well as after rescoring the 8-best list using a very large 5-gram language model (column 3). For comparison, we also report results using a traditional state-of-the-art low frame rate (LFR) The model is decoded using a pruned, first-pass, 5-gram language model, which uses a vocabulary of millions of words, as well as an expert-curated pronunciation dictionary. As before, we report results both before and after second-pass lattice rescoring. The gains after MWER training are even larger after second-pass rescoring, improving the baseline uni- and bi-directional LAS systems by 8.2% and 6.1%, respectively. Finally, we note that after MWER training the grapheme-based uni-directional LAS system matches the performance of a state-of-the-art traditional CD-phoneme-based ASR system.