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.

翻译：神经网络剪枝能有效压缩自动语音识别（ASR）模型。然而，在多语言ASR中，语言无关的剪枝可能导致某些语言的性能严重下降，因为语言无关的剪枝掩码可能不适用于所有语言，并会丢弃重要的语言特定参数。本文提出ASR路径——一种稀疏多语言ASR模型，该模型激活语言特定的子网络（"路径"），从而显式学习每种语言的参数。通过重叠子网络，共享参数还能通过联合多语言训练实现低资源语言的知识迁移。我们提出了一种新颖的ASR路径学习算法，并使用流式RNN-T模型在4种语言上评估了该方法。实验表明，所提出的ASR路径模型不仅优于稠密模型和语言无关剪枝模型，而且相比单语言稀疏模型，在低资源语言上取得了更优性能。