Learning ASR pathways: A sparse multilingual ASR model

• URL: http://arxiv.org/abs/2209.05735v4
• Date: Thu, 28 Sep 2023 20:24:33 GMT
• Title: Learning ASR pathways: A sparse multilingual ASR model
• Authors: Mu Yang, Andros Tjandra, Chunxi Liu, David Zhang, Duc Le, Ozlem Kalinli
• Abstract summary: We present ASR pathways, a sparse multilingual ASR model that activates language-specific sub-networks ("pathways") With the overlapping sub-networks, the shared parameters can also enable knowledge transfer for lower-resource languages via joint multilingual training. Our proposed ASR pathways outperform both dense models and a language-agnostically pruned model, and provide better performance on low-resource languages.
• Score: 31.147484652643282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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