This paper addresses challenges in integrating new languages into a
pre-trained multilingual automatic speech recognition (mASR) system,
particularly in scenarios where training data for existing languages is limited
or unavailable. The proposed method employs a dual-pipeline with low-rank
adaptation (LoRA). It maintains two data flow pipelines-one for existing
languages and another for new languages. The primary pipeline follows the
standard flow through the pre-trained parameters of mASR, while the secondary
pipeline additionally utilizes language-specific parameters represented by LoRA
and a separate output decoder module. Importantly, the proposed approach
minimizes the performance degradation of existing languages and enables a
language-agnostic operation mode, facilitated by a decoder selection strategy.
We validate the effectiveness of the proposed method by extending the
pre-trained Whisper model to 19 new languages from the FLEURS dataset

通过采用双流水线及低秩适应的方法，该研究应对多语言预训练自动语音识别系统中将新语言整合进去的挑战，减少既有语言性能的降低，并实现了一种与语言无关的操作模式。