  With advances in deep learning, neural network based speech enhancement (SE)
has developed rapidly in the last decade. Meanwhile, the self-supervised
pre-trained model and vector quantization (VQ) have achieved excellent
performance on many speech-related tasks, while they are less explored on SE.
As it was shown in our previous work that utilizing a VQ module to discretize
noisy speech representations is beneficial for speech denoising, in this work
we therefore study the impact of using VQ at different layers with different
number of codebooks. Different VQ modules indeed enable to extract
multiple-granularity speech features. Following an attention mechanism, the
contextual features extracted by a pre-trained model are fused with the local
features extracted by the encoder, such that both global and local information
are preserved to reconstruct the enhanced speech. Experimental results on the
Valentini dataset show that the proposed model can improve the SE performance,
where the impact of choosing pre-trained models is also revealed.
