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。

多语种 ASR 中新语种整合的低秩自适应双通道模型

Dual-Pipeline with Low-Rank Adaptation for New Language Integration in  Multilingual ASR

Modern multilingual models are trained on concatenated text from multiple
languages in hopes of conferring benefits to each (positive transfer), with the
most pronounced benefits accruing to low-resource languages. However, recent
work has shown that this approach can degrade performance on high-resource
languages, a phenomenon known as negative interference. In this paper, we
present the first systematic study of negative interference. We show that,
contrary to previous belief, negative interference also impacts low-resource
languages. While parameters are maximally shared to learn language-universal
structures, we demonstrate that language-specific parameters do exist in
multilingual models and they are a potential cause of negative interference.
Motivated by these observations, we also present a meta-learning algorithm that
obtains better cross-lingual transferability and alleviates negative
interference, by adding language-specific layers as meta-parameters and
training them in a manner that explicitly improves shared layers'
generalization on all languages. Overall, our results show that negative
interference is more common than previously known, suggesting new directions
for improving multilingual representations.

本研究通过系统研究表明，多语言模型存在负干扰问题，即共享参数对高资源语言也有削弱效果。我们发现多语言模型中存在语言特定的参数，用元学习算法训练这些参数可以规避负干扰问题，提高跨语言泛化能力。