  Although Automatic Speech Recognition (ASR) systems have achieved human-like
performance for a few languages, the majority of the world's languages do not
have usable systems due to the lack of large speech datasets to train these
models. Cross-lingual transfer is an attractive solution to this problem,
because low-resource languages can potentially benefit from higher-resource
languages either through transfer learning, or being jointly trained in the
same multilingual model. The problem of cross-lingual transfer has been well
studied in ASR, however, recent advances in Self Supervised Learning are
opening up avenues for unlabeled speech data to be used in multilingual ASR
models, which can pave the way for improved performance on low-resource
languages. In this paper, we survey the state of the art in multilingual ASR
models that are built with cross-lingual transfer in mind. We present best
practices for building multilingual models from research across diverse
languages and techniques, discuss open questions and provide recommendations
for future work.
