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.

提出了两种简单和参数高效的方法：语言提示调优和帧级语言适配器，用于分别增强可配置语言和语言无关的多语言语音识别，同时探索了使用参数高效的微调方法整合这两种方法的可行性。实验证明了我们提出的方法在七种语言下均具有显著的性能提升。