SkinAugment: Auto-Encoding Speaker Conversions for Automatic Speech Translation
Table 1: Value of machine-translated transcripts combined with SkinAugment on AST LibriSpeech. We use 16 skins applied to 25% of the corpus.
['Data', 'BLEU']
Thus far, our synthetic data on the AST task has been generated by transforming original AST samples with either SkinAugment or SpecAugment(-p). However, adding translated data as weak supervision in our low-resource scenario improves performance significantly. ’s dataset (\say− AT). We speculate that the abysmal performance of the baseline AST LibriSpeech − AT is due to data scarcity, and that removing automatic translations for + MT helps because they are of lower quality.