Dual-Pipeline with Low-Rank Adaptation for New Language Integration in Multilingual ASR
<|reference_start|>Dual-Pipeline with Low-Rank Adaptation for New Language Integration in Multilingual ASR: 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<|reference_end|>
@article{khassanov2024dual-pipeline,
    title={Dual-Pipeline with Low-Rank Adaptation for New Language Integration in
  Multilingual ASR},
    author={Yerbolat Khassanov, Zhipeng Chen, Tianfeng Chen, Tze Yuang Chong, Wei
  Li, Jun Zhang, Lu Lu, Yuxuan Wang},
    journal={arXiv preprint arXiv:2406.07842},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07842},
    primaryClass={eess.AS cs.CL}
}