  Multilingual intelligent assistants, such as ChatGPT, have recently gained
popularity. To further expand the applications of multilingual artificial
intelligence assistants and facilitate international communication, it is
essential to enhance the performance of multilingual speech recognition, which
is a crucial component of speech interaction. In this paper, we propose two
simple and parameter-efficient methods: language prompt tuning and frame-level
language adapter, to respectively enhance language-configurable and
language-agnostic multilingual speech recognition. Additionally, we explore the
feasibility of integrating these two approaches using parameter-efficient
fine-tuning methods. Our experiments demonstrate significant performance
improvements across seven languages using our proposed methods.
