  In real-time speech communication systems, speech signals are often degraded
by multiple distortions. Recently, a two-stage Repair-and-Denoising network
(RaD-Net) was proposed with superior speech quality improvement in the ICASSP
2024 Speech Signal Improvement (SSI) Challenge. However, failure to use future
information and constraint receptive field of convolution layers limit the
system's performance. To mitigate these problems, we extend RaD-Net to its
upgraded version, RaD-Net 2. Specifically, a causality-based knowledge
distillation is introduced in the first stage to use future information in a
causal way. We use the non-causal repairing network as the teacher to improve
the performance of the causal repairing network. In addition, in the second
stage, complex axial self-attention is applied in the denoising network's
complex feature encoder/decoder. Experimental results on the ICASSP 2024 SSI
Challenge blind test set show that RaD-Net 2 brings 0.10 OVRL DNSMOS
improvement compared to RaD-Net.
