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.

本研究采用自注意力机制，设计了一个大型的多语言端到端语音识别系统，使用阿拉伯语、英语和法语训练。我们评估了该系统在处理单语言、多方言以及代码转换等方面的表现，并与当前最先进的系统进行了比较。结果表明，我们的模型强于最先进的单语言方言阿拉伯语和代码转换阿拉伯语语音识别系统。