Adding a language-identification block trained with non-peaky CTC and injecting the resulting language posteriors reduces mixed-error rate on Mandarin-English SEAME by about 0.5 to 0.8 percent absolute over the D-MoE baseline.
Experimental setup All experiments are conducted using the SEAME dataset [28], a spontaneous code -switching corpus recorded by Southeast Asian speakers
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Enhancing Code-Switching ASR Leveraging Non-Peaky CTC Loss and Deep Language Posterior Injection
Adding a language-identification block trained with non-peaky CTC and injecting the resulting language posteriors reduces mixed-error rate on Mandarin-English SEAME by about 0.5 to 0.8 percent absolute over the D-MoE baseline.