Alleviating the Loss-Metric Mismatch in Supervised Single-Channel Speech Enhancement

In this paper, we study the loss-metric mismatch problem of supervised single-channel speech enhancement system. Most of the existing speech enhancement systems achieve unsatisfying performance since their empirically selected loss functions have semantic gaps with the non-differentiable evaluation metrics, a.k.a., the loss-metric mismatch problem. In this work, we propose a simple yet ef?cient method to generate suitable loss functions for the real front-end speech enhancement scenarios to alleviate the loss-metric mismatch problem. Speci?cally, we adopt the function smoothing technique and approximate the non-differentiable evaluation metrics by a set of basis functions and their linear combination. Experimental results demonstrate that the loss function generated by our method helps the speech enhancement system achieve remarkable performance in most evaluation metrics than the traditional empirically selected ones.