Building a multilingual Automated Speech Recognition (ASR) system in a linguistically diverse country like India can be a challenging task due to the differences in scripts and the limited availability of speech data. This problem can be solved by exploiting the fact that many of these languages are phonetically similar. These languages can be converted into a Common Label Set (CLS) by mapping similar sounds to common labels. In this paper, new approaches are explored and compared to improve the performance of CLS based multilingual ASR model. Specific language information is infused in the ASR model by giving Language ID or using CLS to Native script converter on top of the CLS Multilingual model. These methods give a significant improvement in Word Error Rate (WER) compared to the CLS baseline. These methods are further tried on out-of-distribution data to check their robustness.

翻译：摘要：在印度这样一个语言多样性国家构建多语言自动语音识别（ASR）系统，由于书写系统差异和语音数据有限，是一项具有挑战性的任务。通过利用许多语言在语音上的相似性，可以解决这一问题。这些语言可以通过将相似语音映射到通用标签集（CLS）来转换为通用标签集。本文探索并比较了新的方法，以提升基于CLS的多语言ASR模型的性能。通过提供语言ID或在CLS多语言模型之上使用CLS到原生脚本转换器，将特定语言信息注入ASR模型。与CLS基线相比，这些方法在词错误率（WER）上取得了显著改进。此外，这些方法还针对分布外数据进行了测试，以验证其鲁棒性。