  Internal Language Model Estimation (ILME) based language model (LM) fusion
has been shown significantly improved recognition results over conventional
shallow fusion in both intra-domain and cross-domain speech recognition tasks.
In this paper, we attempt to apply our ILME method to cross-domain
code-switching speech recognition (CSSR) work. Specifically, our curiosity
comes from several aspects. First, we are curious about how effective the
ILME-based LM fusion is for both intra-domain and cross-domain CSSR tasks. We
verify this with or without merging two code-switching domains. More
importantly, we train an end-to-end (E2E) speech recognition model by means of
merging two monolingual data sets and observe the efficacy of the proposed
ILME-based LM fusion for CSSR. Experimental results on SEAME that is from
Southeast Asian and another Chinese Mainland CS data set demonstrate the
effectiveness of the proposed ILME-based LM fusion method.
