  Speech enhancement significantly improves the clarity and intelligibility of
speech in noisy environments, improving communication and listening
experiences. In this paper, we introduce a novel pretraining feature-guided
diffusion model tailored for efficient speech enhancement, addressing the
limitations of existing discriminative and generative models. By integrating
spectral features into a variational autoencoder (VAE) and leveraging
pre-trained features for guidance during the reverse process, coupled with the
utilization of the deterministic discrete integration method (DDIM) to
streamline sampling steps, our model improves efficiency and speech enhancement
quality. Demonstrating state-of-the-art results on two public datasets with
different SNRs, our model outshines other baselines in efficiency and
robustness. The proposed method not only optimizes performance but also
enhances practical deployment capabilities, without increasing computational
demands.
