  End-to-end multilingual ASR has become more appealing because of several
reasons such as simplifying the training and deployment process and positive
performance transfer from high-resource to low-resource languages. However,
scaling up the number of languages, total hours, and number of unique tokens is
not a trivial task. This paper explores large-scale multilingual ASR models on
70 languages. We inspect two architectures: (1) Shared embedding and output and
(2) Multiple embedding and output model. In the shared model experiments, we
show the importance of tokenization strategy across different languages. Later,
we use our optimal tokenization strategy to train multiple embedding and output
model to further improve our result. Our multilingual ASR achieves 13.9%-15.6%
average WER relative improvement compared to monolingual models. We show that
our multilingual ASR generalizes well on an unseen dataset and domain,
achieving 9.5% and 7.5% WER on Multilingual Librispeech (MLS) with zero-shot
and finetuning, respectively.
