  It is very challenging for speech enhancement methods to achieves robust
performance under both high signal-to-noise ratio (SNR) and low SNR
simultaneously. In this paper, we propose a method that integrates an SNR-based
teachers-student technique and time-domain U-Net to deal with this problem.
Specifically, this method consists of multiple teacher models and a student
model. We first train the teacher models under multiple small-range SNRs that
do not coincide with each other so that they can perform speech enhancement
well within the specific SNR range. Then, we choose different teacher models to
supervise the training of the student model according to the SNR of the
training data. Eventually, the student model can perform speech enhancement
under both high SNR and low SNR. To evaluate the proposed method, we
constructed a dataset with an SNR ranging from -20dB to 20dB based on the
public dataset. We experimentally analyzed the effectiveness of the SNR-based
teachers-student technique and compared the proposed method with several
state-of-the-art methods.
