Language fusion via adapters for low-resource speech recognition

Data scarcity makes low -resource speech recognition systems suffer from severe overfitting. Although finetuning addresses this issue to some extent, it leads to parameter -inefficient training. In this paper, a novel language knowledge fusion method, named LanFusion, is proposed. It is built on the recent popular adaptertuning technique, thus maintaining better parameter efficiency compared with conventional fine-tuning methods. LanFusion is a two -stage method. Specifically, multiple adapters are first trained on several source languages to extract language -specific and language -invariant knowledge. Then, the trained adapters are retrained on the target low -resource language to fuse the learned knowledge. Compared with Vanilla -adapter, LanFusion obtains a relative average word error rate (WER) reduction of 9.8% and 8.6% on the Common Voice and FLEURS corpora, respectively. Extensive experiments demonstrate the proposed method is not only simple and effective but also parameter -efficient. Besides, using source languages that are geographically similar to the target language yields better results on both datasets.