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This paper describes our NPU-ASLP system submitted to the ISCSLP 2022Magichub Code-Switching ASR Challenge. In this challenge, we first exploreseveral popular end-to-end ASR architectures and training strategies, includingbi-encoder, language-aware encoder (LAE) and mixture of experts (MoE). Toimprove our system's language modeling ability, we further attempt the internallanguage model as well as the long context language model. Given the limitedtraining data in the challenge, we further investigate the effects of dataaugmentation, including speed perturbation, pitch shifting, speech codec,SpecAugment and synthetic data from text-to-speech (TTS). Finally, we exploreROVER-based score fusion to make full use of complementary hypotheses fromdifferent models. Our submitted system achieves 16.87% on mix error rate (MER)on the test set and comes to the 2nd place in the challenge ranking.

The NPU-ASLP System for The ISCSLP 2022 Magichub Code-Swiching ASR Challenge