Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient
  Pruning of A Multilingual ASR Model
Authors: name: Jiamin Xie, name: Ke Li, name: Jinxi Guo, name: Andros Tjandra, name: Yuan Shangguan, name: Leda Sari, name: Chunyang Wu, name: Junteng Jia, name: Jay Mahadeokar, name: Ozlem Kalinli

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.