{"id":898095,"date":"2022-11-14T21:48:47","date_gmt":"2022-11-15T05:48:47","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/"},"modified":"2022-11-14T21:50:50","modified_gmt":"2022-11-15T05:50:50","slug":"breaking-trade-offs-in-speech-separation-with-sparsely-gated-mixture-of-experts","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/breaking-trade-offs-in-speech-separation-with-sparsely-gated-mixture-of-experts\/","title":{"rendered":"Breaking trade-offs in speech separation with sparsely-gated mixture of experts"},"content":{"rendered":"<blockquote class=\"abstract mathjax\"><p>Several trade-offs need to be balanced when employing monaural speech separation (SS) models in conversational automatic speech recognition (ASR) systems. A larger SS model generally achieves better output quality at an expense of higher computation, meanwhile, a better SS model for overlapping speech often produces distorted output for non-overlapping speech. This paper addresses these trade-offs with a sparsely-gated mixture-of-experts (MoE). The sparsely-gated MoE architecture allows the separation models to be enlarged without compromising the run-time efficiency, which also helps achieve a better separation-distortion trade-off. To further reduce the speech distortion without compromising the SS capability, a multi-gate MoE framework is also explored, where different gates handle non-overlapping and overlapping frames differently. ASR experiments are conducted by using a simulated dataset for measuring both the speech separation accuracy and the speech distortion. Two advanced SS models, Conformer and WavLM-based models, are used as baselines. The sparsely-gated MoE models show a superior SS capability with less speech distortion, meanwhile marginally increasing the run-time computational cost. Experimental results using real conversation recordings are also presented, showing MoE&#8217;s effectiveness in an end-to-end evaluation setting.<\/p><\/blockquote>\n<div class=\"metatable\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Several trade-offs need to be balanced when employing monaural speech separation (SS) models in conversational automatic speech recognition (ASR) systems. A larger SS model generally achieves better output quality at an expense of higher computation, meanwhile, a better SS model for overlapping speech often produces distorted output for non-overlapping speech. This paper addresses these trade-offs 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