  With the advent of globalization, there is an increasing demand for
multilingual automatic speech recognition (ASR), handling language and
dialectal variation of spoken content. Recent studies show its efficacy over
monolingual systems. In this study, we design a large multilingual end-to-end
ASR using self-attention based conformer architecture. We trained the system
using Arabic (Ar), English (En) and French (Fr) languages. We evaluate the
system performance handling: (i) monolingual (Ar, En and Fr); (ii)
multi-dialectal (Modern Standard Arabic, along with dialectal variation such as
Egyptian and Moroccan); (iii) code-switching -- cross-lingual (Ar-En/Fr) and
dialectal (MSA-Egyptian dialect) test cases, and compare with current
state-of-the-art systems. Furthermore, we investigate the influence of
different embedding/character representations including character vs
word-piece; shared vs distinct input symbol per language. Our findings
demonstrate the strength of such a model by outperforming state-of-the-art
monolingual dialectal Arabic and code-switching Arabic ASR.
