Learning not to Discriminate: Task Agnostic Learning for Improving Monolingual and Code-switched Speech Recognition
Table 3: WER[%] of pooled (Exp3), fine-tuned pooled model [shah2020learning] (Exp4), adversarial task agnostic pooled model (Exp5) and multi-task adversarial speech recognition model (Exp6)
['[BOLD] Test Set', '[ITALIC]  [BOLD] Exp3', '[ITALIC]  [BOLD] Exp4', '[ITALIC]  [BOLD] Exp5', '[ITALIC]  [BOLD] Exp6']
We can see that Exp5 performs better than Exp3 for all test sets. This indicates that the model benefits from adversarial task-discriminative training for improving both monolingual and code-switched speech recognition. The performance comparison of Multi-task adversarial model (Exp6), adversarial task We can see that the Multi-task adversarial model (Exp6) outperforms all other models on all monolingual and code-switched test sets. The improved WER can be attributed to the fact that adversarial training helps the shared layer parameters to learn task invariant monolingual and code-switched features, and having task specific layers further help in improving accuracy for individual tasks.