Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition
Table 4: WER [%] of models fine-tuned with varying amounts of data (D)
['[BOLD] Test Set', '[ITALIC]  [BOLD] Exp3', '[ITALIC]  [BOLD] Exp5', '[ITALIC]  [BOLD] D=75%', '[ITALIC]  [BOLD] D=50%', '[ITALIC]  [BOLD] D=25%']
For each epoch during fine-tuning, we randomly sample D% of the data from the total CS data available. Exp5 is fine-tuned on all the data. More importantly, we find that the loss in performance in monolingual test sets (1-2% absolute WER) is mitigated by fine-tuning on less code-switched data.