  With the development of deep learning, neural network-based speech
enhancement (SE) models have shown excellent performance. Meanwhile, it was
shown that the development of self-supervised pre-trained models can be applied
to various downstream tasks. In this paper, we will consider the application of
the pre-trained model to the real-time SE problem. Specifically, the encoder
and bottleneck layer of the DEMUCS model are initialized using the
self-supervised pretrained WavLM model, the convolution in the encoder is
replaced by causal convolution, and the transformer encoder in the bottleneck
layer is based on causal attention mask. In addition, as discretizing the noisy
speech representations is more beneficial for denoising, we utilize a
quantization module to discretize the representation output from the bottleneck
layer, which is then fed into the decoder to reconstruct the clean speech
waveform. Experimental results on the Valentini dataset and an internal dataset
show that the pre-trained model based initialization can improve the SE
performance and the discretization operation suppresses the noise component in
the representations to some extent, which can further improve the performance.
