  Training multilingual automatic speech recognition (ASR) systems is
challenging because acoustic and lexical information is typically language
specific. Training multilingual system for Indic languages is even more tougher
due to lack of open source datasets and results on different approaches. We
compare the performance of end to end multilingual speech recognition system to
the performance of monolingual models conditioned on language identification
(LID). The decoding information from a multilingual model is used for language
identification and then combined with monolingual models to get an improvement
of 50% WER across languages. We also propose a similar technique to solve the
Code Switched problem and achieve a WER of 21.77 and 28.27 over Hindi-English
and Bengali-English respectively. Our work talks on how transformer based ASR
especially wav2vec 2.0 can be applied in developing multilingual ASR and code
switched ASR for Indic languages.
