Sequence-to-sequence Automatic Speech Recognition with word embedding regularization and fused decoding
Table 1: Speech recognition word error rate (%) on LibriSpeech with 460 hours training data (high resource setting).
['Index', 'Word Embedding', 'Fused', 'Word Embedding', '(I) WER (%) Clean', '(I) WER (%) Clean', '(I) WER (%) Other', '(I) WER (%) Other', '(II) WER (%), w/ RNN-LM Clean', '(II) WER (%), w/ RNN-LM Clean', '(II) WER (%), w/ RNN-LM Other', '(II) WER (%), w/ RNN-LM Other']
We also combined our method with joint RNN-LM decoding [hori2017advances] where the RNN-LM was separately trained on the identical text corpus used by word embedding (columns (II) v.s. (I)). Performance Study We first compare our proposed methods against the baseline, and found consistent improvements when only limited training data was available (rows(b)(c) v.s. (a), columns(I)(II)).