  Neural network pruning compresses automatic speech recognition (ASR) models
effectively. However, in multilingual ASR, language-agnostic pruning may lead
to severe performance drops on some languages because language-agnostic pruning
masks may not fit all languages and discard important language-specific
parameters. In this work, we present ASR pathways, a sparse multilingual ASR
model that activates language-specific sub-networks ("pathways"), such that the
parameters for each language are learned explicitly. With the overlapping
sub-networks, the shared parameters can also enable knowledge transfer for
lower-resource languages via joint multilingual training. We propose a novel
algorithm to learn ASR pathways, and evaluate the proposed method on 4
languages with a streaming RNN-T model. Our proposed ASR pathways outperform
both dense models and a language-agnostically pruned model, and provide better
performance on low-resource languages compared to the monolingual sparse
models.
