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- W4309128996 abstract "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." @default.
- W4309128996 created "2022-11-23" @default.
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- W4309128996 date "2022-11-11" @default.
- W4309128996 modified "2023-10-16" @default.
- W4309128996 title "Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts" @default.
- W4309128996 doi "https://doi.org/10.48550/arxiv.2211.06493" @default.
- W4309128996 hasPublicationYear "2022" @default.
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