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 c...
| Authors | Rostislav Makarov, Timo Gerkmann |
|---|---|
| Primary category | eess.AS |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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