Massively Multilingual Adversarial Speech Recognition
Table 3: Word error rate (%) comparison of adaptation of models pretrained on: Quechuan and Cyrillic-script languages (Que+Cyr), languages phonologically and phonetically similar to the target (Phon/Inv), geographically proximate languages (Geo), and a massively multilingual set of languages (100-lang). In each case we compared the average relative WER change when adding auxiliary phoneme and language-adversarial objectives (+phn+adv). Dashed entries had phonology and phonetic inventories that weren’t well attested in URIEL, so were not assessed.
['[EMPTY]', 'Mono', 'Que+Cyr -', 'Que+Cyr +phn+adv', 'Que+Cyr +phn+adv', 'Phonology -', 'Phonology +phn+adv', 'Phonology +phn+adv', 'Geo -', 'Geo +phn+adv', 'Geo +phn+adv', '100-lang -', '100-lang +phn+adv', '100-lang +phn+adv']
Results from pretraining on Que+Cyr showed either objective to help on average, and that the effects are complementary. Because of this, we opted to include them together in subsequent experimentation. We evaluated this best performing model on the larger set of other evaluation languages. Geo and 100-lang benefitted comparably from the objectives on average, while Phon/Inv did less so. Que+Cyr benefitted the most. This suggests that the objectives may help more when pretraining languages are orthographically, phonetically and phonologically diverse.