Whole-Word Segmental Speech Recognition with Acoustic Word Embeddings
Table 1: Comparison of initialization with phone CTC vs. AWE/AGWE, in terms of SWB dev WER (%). “AWE init” refers to initialization of the parameters of fAWE. “AGWE init” refers to initialization of the rows of A(2).
['System', 'Vocab size 5K', 'Vocab size 10K', 'Vocab size 20K']
We find that, unlike our A2W CTC models and those of prior work [settle2019_a2w, yu2018_amt], the segmental models do not improve significantly (or at all) with larger vocabulary. One possible reason is that word representations in segmental models, especially for rare words, are harder to learn as they must be robust to variations in segment duration and content. Segmental models may require more data and overfit when many rare words are included. We find that it is important to set a larger dropout value as the vocabulary size increases. Initialization with the pre-trained AWE model reduces WER by 1–2% over phone CTC initialization. Initialization of A(2) This is consistent with our expectation: Since the AGWEs are composed from character sequences, they are less impacted by vocabulary size, helping with recognition of rare words.