  Multilingual speech recognition for both monolingual and code-switching
speech is a challenging task. Recently, based on the Mixture of Experts (MoE),
many works have made good progress in multilingual and code-switching ASR, but
present huge computational complexity with the increase of supported languages.
In this work, we propose a computation-efficient network named Language-Routing
Mixture of Experts (LR-MoE) for multilingual and code-switching ASR. LR-MoE
extracts language-specific representations through the Mixture of Language
Experts (MLE), which is guided to learn by a frame-wise language routing
mechanism. The weight-shared frame-level language identification (LID) network
is jointly trained as the shared pre-router of each MoE layer. Experiments show
that the proposed method significantly improves multilingual and code-switching
speech recognition performances over baseline with comparable computational
efficiency.
