  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.

Audio and Speech Processing;Computation and Language;Machine Learning;Sound