Learning ASR pathways: A sparse multilingual ASR model

Mu Yang; Andros Tjandra; Chunxi Liu; David Zhang; Duc Le; Ozlem Kalinli

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arXiv · 2209.05735

Learning ASR pathways: A sparse multilingual ASR model

Mu Yang·Andros Tjandra·Chunxi Liu·David Zhang·Duc Le·Ozlem Kalinli

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Abstract

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.

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Mu Yang, Andros Tjandra, Chunxi Liu, David Zhang, Duc Le, Ozlem Kalinli. 2022-09-13. Learning ASR pathways: A sparse multilingual ASR model. https://arxiv.org/abs/2209.05735

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