Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition
Table 2: Baseline Word Error Rates (WER)
['[BOLD] Test Set', '[ITALIC]  [BOLD] Exp1', '[ITALIC]  [BOLD] Exp2', '[ITALIC]  [BOLD] Exp3']
Exp3, which is the pooled model consisting of monolingual and code-switched data performs best on all test sets. Exp1 performs better on monolingual test sets than Exp2, and the reverse is true for code-switched test sets, as expected. The n-gram Language Model (LM), trained with training transcriptions, is used during decoding. We do not apply any text normalization during LM training, which could take care removing non-standard spellings, known spelling variations that would further reduce all the WERs. Cross-transcription and borrowed words lower the accuracy on code-switched test sets, which can be mitigated by using a modified version of the WER, such as poWER [srivastava2018homophone]. However, we use the regular version of WER metric in this work to enable a fairer comparison with monolingual test sets.