This paper addresses the challenge of integrating low-resource languages into multilingual automatic speech recognition (ASR) systems. We introduce a novel application of weighted cross-entropy, typically used for unbalanced datasets, to facilitate the integration of low-resource languages into pre-trained multilingual ASR models within the context of continual multilingual learning. We fine-tune the Whisper multilingual ASR model on five high-resource languages and one low-resource language, employing language-weighted dynamic cross-entropy and data augmentation. The results show a remarkable 6.69% word error rate (WER) reduction for the low-resource language compared to the fine-tuned model without applying our approach, and a 48.86% WER reduction compared to the original Whisper model. In addition, our approach yields an average WER reduction of 3.29% across the six languages, showing no degradation for the high-resource languages.

本研究解决了将低资源语言整合到多语种自动语音识别系统中的挑战。通过在持续多语种学习背景下引入加权交叉熵的方法，研究表明该方法对低资源语言的语音识别效果显著提高，词错误率减少了6.69%，并且在六种语言中平均减少了3.29%的错误率，而高资源语言的表现未受到影响。

多语种语音识别中的低资源语言加权交叉熵