  We study training a single acoustic model for multiple languages with the aim
of improving automatic speech recognition (ASR) performance on low-resource
languages, and over-all simplifying deployment of ASR systems that support
diverse languages. We perform an extensive benchmark on 51 languages, with
varying amount of training data by language(from 100 hours to 1100 hours). We
compare three variants of multilingual training from a single joint model
without knowing the input language, to using this information, to multiple
heads (one per language cluster). We show that multilingual training of ASR
models on several languages can improve recognition performance, in particular,
on low resource languages. We see 20.9%, 23% and 28.8% average WER relative
reduction compared to monolingual baselines on joint model, joint model with
language input and multi head model respectively. To our knowledge, this is the
first work studying multilingual ASR at massive scale, with more than 50
languages and more than 16,000 hours of audio across them.
