Whole-Word Segmental Speech Recognition with Acoustic Word Embeddings
Table 2: WER (%) results on SWB/CH evaluation sets.
['System', 'Vocab 4K/5K', 'Vocab 10K', 'Vocab 20K']
Training with SpecAugment [park2019specaugment] produces an additional gain of ∼1% across all vocabulary sizes. On the SWB test set, our model also improves over all previous models except for the one of [yu2018_amt], which is larger and trained with speed perturbation. On the CH test set performance also improves over CTC except at the largest vocabulary size. We hypothesize that CH has even larger variability within segments, but this remains to be investigated. We leave as future work the incorporation of larger models and speed perturbation.