Adversarial Training for Multilingual Acoustic Modeling
Table 3: WERs (%) of multilingual adversarial models before and after fine-tuning.
['Language', 'WER (%) Adversarial', 'WER (%) +Fine Tuning']
On the other hand, we also fine tune the multilingual adversarial models for each language. This may mean that the language-invariant feature representation is sensitive to per-language fine tuning and was partly lost. One future experiment would be to fine-tune different layers and compare WER changes. On the other hand, since the shared layers are initialized from the multilingual adversarial model, the fine-tuned models still perform significantly better than monolingual models.