这篇论文教你用一个现成的语音分类器直接生成语音，省掉单独训练扩散模型，更轻量高效。
该研究提出一种将传统语音分类器改造为扩散生成主干的方法。从冻结的噪声条件分类器（在log-Mel频谱上）开始，附加一个轻量子网络来复用中间分类器表示，仅训练该子网络，使用去噪得分匹配目标。这种方法实现了单主干模型的条件语音生成，在保持语音质量的同时减少了内存占用和计算成本。
Repurposing a Speech Classifier for Guided Diffusion-Based Speech Generation
Classifier guidance is a way to control diffusion generation by using a noise-conditioned classifier to steer the sampling process toward a target class. One drawback of classifier guidance is that it requires two separately trained models: a classifier and a diffusion model. We therefore study a more compact alternative in which a conventionally trained speech classifier is repurposed as the backbone for diffusion generation. Starting from a frozen noise-conditioned classifier in log-Mel space, we attach a lightweight subnetwork that reuses intermediate classifier representations and train only this subnetwork under a Denoising Score Matching objective. Our work shows that a pretrained classifier can be repurposed for conditional generation, providing an appealing bridge between discriminative modeling and conditional speech synthesis resulting in high speech quality within a single-backbone model, with reduced memory footprint and computational cost.