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.

通过整合频谱特征到变分自编码器（VAE）中，并在反向过程中利用预训练特征进行引导，结合确定性离散积分方法（DDIM）来简化抽样步骤，我们的模型提高了效率和语音增强质量，同时在两个不同信噪比的公共数据集上展示出最先进的结果，超越了其他基线方法在效率和稳健性方面。所提出的方法不仅优化了性能，还增强了实际部署能力，而不增加计算需求。