  Employing a monaural speech separation (SS) model as a front-end for
automatic speech recognition (ASR) involves balancing two kinds of trade-offs.
First, while a larger model improves the SS performance, it also requires a
higher computational cost. Second, an SS model that is more optimized for
handling overlapped speech is likely to introduce more processing artifacts in
non-overlapped-speech regions. In this paper, we address these trade-offs with
a sparsely-gated mixture-of-experts (MoE) architecture. Comprehensive
evaluation results obtained using both simulated and real meeting recordings
show that our proposed sparsely-gated MoE SS model achieves superior separation
capabilities with less speech distortion, while involving only a marginal
run-time cost increase.
