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Recently, to mitigate the confusion between different languages incode-switching (CS) automatic speech recognition (ASR), the conditionallyfactorized models, such as the language-aware encoder (LAE), explicitlydisregard the contextual information between different languages. However, thisinformation may be helpful for ASR modeling. To alleviate this issue, wepropose the LAE-ST-MoE framework. It incorporates speech translation (ST) tasksinto LAE and utilizes ST to learn the contextual information between differentlanguages. It introduces a task-based mixture of expert modules, employingseparate feed-forward networks for the ASR and ST tasks. Experimental resultson the ASRU 2019 Mandarin-English CS challenge dataset demonstrate that,compared to the LAE-based CTC, the LAE-ST-MoE model achieves a 9.26% mix errorreduction on the CS test with the same decoding parameter. Moreover, thewell-trained LAE-ST-MoE model can perform ST tasks from CS speech to Mandarinor English text.

LAE-ST-MoE: Boosted Language-Aware Encoder Using Speech Translation Auxiliary Task for E2E Code-switching ASR