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.

翻译：多语言智能助手（如ChatGPT）近年来广受欢迎。为进一步拓展多语言人工智能助手的应用场景、促进国际交流，提升作为语音交互核心组件的多语言语音识别性能至关重要。本文提出了两种简单且参数高效的方法：语言提示调优（language prompt tuning）和帧级语言适配器（frame-level language adapter），分别增强语言可配置型与语言无关型多语言语音识别。此外，我们探索了利用参数高效微调方法整合这两种技术的可行性。实验表明，所提方法在七种语言上均实现了显著的性能提升。