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.

研究了基于通用标签集合（CLS）的多语言 ASR 的性能提升，通过给 ASR 模型添加语言 ID 或使用 CLS 到本地脚本转换器，可以将特定的语言信息注入到 ASR 模型中。这些方法与基线模型相比在词错误率上得到了显着改善，并在分布数据上进行了有效性测试。

利用 CLS 和语言标注来增强多语种 ASR 的 Tag-Team 方法

The Tag-Team Approach: Leveraging CLS and Language Tagging for Enhancing  Multilingual ASR

In a multilingual country like India, multilingual Automatic Speech
Recognition (ASR) systems have much scope. Multilingual ASR systems exhibit
many advantages like scalability, maintainability, and improved performance
over the monolingual ASR systems. However, building multilingual systems for
Indian languages is challenging since different languages use different scripts
for writing. On the other hand, Indian languages share a lot of common sounds.
Common Label Set (CLS) exploits this idea and maps graphemes of various
languages with similar sounds to common labels. Since Indian languages are
mostly phonetic, building a parser to convert from native script to CLS is
easy. In this paper, we explore various approaches to build multilingual ASR
models. We also propose a novel architecture called Encoder-Decoder-Decoder for
building multilingual systems that use both CLS and native script labels. We
also analyzed the effectiveness of CLS-based multilingual systems combined with
machine transliteration.

本研究探讨了建立多语种自动语音识别系统的方法，提出了一种名为 “Encoder-Decoder-Decoder” 的新型架构，将常见标签集与本土语音标签相结合，同时分析了基于常见标签集的多语种系统与机器转写相结合的有效性。