  Recent years have witnessed significant progress in multilingual automatic
speech recognition (ASR), driven by the emergence of end-to-end (E2E) models
and the scaling of multilingual datasets. Despite that, two main challenges
persist in multilingual ASR: language interference and the incorporation of new
languages without degrading the performance of the existing ones. This paper
proposes LoRA-Whisper, which incorporates LoRA matrix into Whisper for
multilingual ASR, effectively mitigating language interference. Furthermore, by
leveraging LoRA and the similarities between languages, we can achieve better
performance on new languages while upholding consistent performance on original
ones. Experiments on a real-world task across eight languages demonstrate that
our proposed LoRA-Whisper yields a relative gain of 18.5% and 23.0% over the
baseline system for multilingual ASR and language expansion respectively.
