  Code-switching in automatic speech recognition (ASR) is an important
challenge due to globalization. Recent research in multilingual ASR shows
potential improvement over monolingual systems. We study key issues related to
multilingual modeling for ASR through a series of large-scale ASR experiments.
Our innovative framework deploys a multi-graph approach in the weighted finite
state transducers (WFST) framework. We compare our WFST decoding strategies
with a transformer sequence to sequence system trained on the same data. Given
a code-switching scenario between Arabic and English languages, our results
show that the WFST decoding approaches were more suitable for the
intersentential code-switching datasets. In addition, the transformer system
performed better for intrasentential code-switching task. With this study, we
release an artificially generated development and test sets, along with
ecological code-switching test set, to benchmark the ASR performance.
