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。