  Diffusion-based generative models (DGMs) have recently attracted attention in
speech enhancement research (SE) as previous works showed a remarkable
generalization capability. However, DGMs are also computationally intensive, as
they usually require many iterations in the reverse diffusion process (RDP),
making them impractical for streaming SE systems. In this paper, we propose to
use discriminative scores from discriminative models in the first steps of the
RDP. These discriminative scores require only one forward pass with the
discriminative model for multiple RDP steps, thus greatly reducing
computations. This approach also allows for performance improvements. We show
that we can trade off between generative and discriminative capabilities as the
number of steps with the discriminative score increases. Furthermore, we
propose a novel streamable time-domain generative model with an algorithmic
latency of 50 ms, which has no significant performance degradation compared to
offline models.
