Sequence-to-sequence Automatic Speech Recognition with word embedding regularization and fused decoding
Table 2: Speech recognition word error rate (%) on LibriSpeech with 100 hours training data (low resource setting). ”w/ LM” indicated jointly RNN-LM decoding [hori2017advances].
['Methods', '(I) WER(%)', '(I) WER(%)', '(II) WER (%)', '(II) WER (%)']
w/ LM” for column(II) indicates joint RNN-LM decoding. Comparing Against Semi-supervised Methods We also listed the performance obtained with the same setting reported by prior works (referred to as ”semi-supervised”) for comparison. Our word embedding regularization surpassed the back-translation data augmentation method [hayashi2018back] (row(d)) yet still performed worse than the adversarial training method [liu2019adversarial] (row(e)). With fused decoding, we further narrowed the gap.