Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter

Ref. [6, 7] introduced an additional language identification (LID) module Multilingual intelligent assistants, such as ChatGPT, have to predict language information, while Ref. [2] treated language recently gained popularity. To further expand the applications information as a special textual token and concatenated of multilingual artificial intelligence (AI) assistants and it to the input of the decoder of the autoregressive speech facilitate international communication, it is essential to enhance recognition model, achieving joint modeling of speech recognition the performance of multilingual speech recognition, and language identification. Ref. [3] provided language which is a crucial component of speech interaction. In this information directly as prior information to speech recognition paper, we propose two simple and parameter-efficient methods: models, this can be achieved by encoding language information language prompt tuning and f rame-level language as a one-hot vector or embedding and concatenating adapter, to respectively enhance language-configurable and it with acoustic features.