Improving speech enhancement by focusing on smaller values using relative loss. Issue 6 (1st August 2020)

• Record Type: Journal Article
• Authors: Li, Hongfeng; Xu, Yanyan; Ke, Dengfeng; Su, Kaile
• Is Part Of: IET signal processing. Volume 14:Issue 6(2020)
• Page Start: 374
• Page End: 384
• Publication Date: 2020-08-01
• Subjects: speech enhancement -- speech intelligibility -- performance evaluation -- learning (artificial intelligence) -- neural nets; absolute differences -- speech quality -- relative loss -- single‐channel speech enhancement -- noisy speech -- ideal ratio mask -- phase‐sensitive mask -- mean square error -- loss function -- absolute error values -- magnitude spectra -- deep learning -- clean speech recovery -- short‐time objective intelligibility -- signal‐to‐distortion ratio -- segmental signal‐to‐noise ratio -- performance evaluation; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2019.0290
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : The task of single‐channel speech enhancement is to restore clean speech from noisy speech. Recently, speech enhancement has been greatly improved with the introduction of deep learning. Previous work proved that using ideal ratio mask or phase‐sensitive mask as intermediation to recover clean speech can yield better performance. In this case, the mean square error is usually selected as the loss function. However, after conducting experiments, the authors find that the mean square error has a problem. It considers absolute error values, meaning that the gradients of the network depend on absolute differences between estimated values and true values, so the points in magnitude spectra with smaller values contribute little to the gradients. To solve this problem, they propose relative loss, which pays more attention to relative differences between magnitude spectra, rather than the absolute differences, and is more in accordance with human sensory characteristics. The perceptual evaluation of speech quality, the short‐time objective intelligibility, the signal‐to‐distortion ratio, and the segmental signal‐to‐noise ratio are used to evaluate the performance of the relative loss. Experimental results show that it can greatly improve speech enhancement by focusing on smaller values.