Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition
Table 3: WER[%] of pooled and fine-tuned models
['[BOLD] Test Set', '[ITALIC]  [BOLD] Exp3', '[ITALIC]  [BOLD] Exp4', '[ITALIC]  [BOLD] Exp5']
We see that fine-tuning the pooled model performs best on all code-switched test sets, while it degrades performance on monolingual Tamil and Telugu test sets. On Gujarati, the fine-tuned model performs the best, and from the CMI we know that Gujarati is the least code-switched of the three datasets. Fine-tuning the monolingual model with code-switched data (Exp4) degrades performance on all three monolingual test sets.