speech generation

Repurposing a Speech Classifier for Guided Diffusion-Based Speech Generation

Researchers Makarov and Gerkmann propose a method to repurpose a conventionally trained speech classifier as the backbone for diffusion-based speech generation. By attaching a lightweight subnetwork and training only that under a Denoising Score Matching objective, they achieve high-quality speech synthesis with reduced memory footprint and computational cost compared to traditional classifier guidance that requires two separately trained models.