Learning not to Discriminate: Task Agnostic Learning for Improving Monolingual and Code-switched Speech Recognition
Table 2: Baseline Word Error Rates (WER in %)
['[BOLD] Test Set', '[ITALIC]  [BOLD] Exp1', '[ITALIC]  [BOLD] Exp2', '[ITALIC]  [BOLD] Exp3']
An n-gram Language Model (LM), trained using transcriptions from the training data is used during decoding. 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.