Neural network pruning offers an effective method for compressing a
multilingual automatic speech recognition (ASR) model with minimal performance
loss. However, it entails several rounds of pruning and re-training needed to
be run for each language. In this work, we propose the use of an adaptive
masking approach in two scenarios for pruning a multilingual ASR model
efficiently, each resulting in sparse monolingual models or a sparse
multilingual model (named as Dynamic ASR Pathways). Our approach dynamically
adapts the sub-network, avoiding premature decisions about a fixed sub-network
structure. We show that our approach outperforms existing pruning methods when
targeting sparse monolingual models. Further, we illustrate that Dynamic ASR
Pathways jointly discovers and trains better sub-networks (pathways) of a
single multilingual model by adapting from different sub-network
initializations, thereby reducing the need for language-specific pruning.

本文提出了一种自适应掩蔽方法，用于高效地修剪多语言自动语音识别模型，并展示了该方法在目标为稀疏的单语言模型时的优势，同时减少了语言特定修剪的需求。