Massively Multilingual Adversarial Speech Recognition
Table 4: Adaptation to the non-target reading in the target language. All language sets use the auxiliary training objectives, which again exhibited an relative gain over the corresponding model without. The relative deltas of 100-lang are with respect to the next closest model on a language-by-language basis.
['ayr', 'Mono 91.4', 'Que +Cyr 86.3', 'Phon +Inv 86.7', 'Geo 87.2', '100-lang  [BOLD] 79.2', '100-lang (-8.2%)']
Massively multilingual pretraining led to substantially better performance than other methods, unlike in the reading adaptation task. For each evaluation language, the 100-lang model outperformed the next best method, with one exception: Indonesian. In that case Geo set performed the best, as the languages were not only geographically proximate, but also consisted entirely of other Austronesian languages.