SEmoEdit: Probing and Harnessing the Editability of Pre-trained Speech Flows
Existing training-based speech emotion editing methods often require substantial task-specific training and can be unstable. This motivates us to investigate whether the pretrained generative dynamics of large-scale text-to-speech (TTS) models can be directly manipulated for training-free emotion editing. To answer this question, we probe the editability of pretrained flow-matching and hybrid TTS models by constructing a controlled test set and systematically diagnosing editing effects along the generative trajectory. Our analysis reveals that pretrained TTS models are substantially editable in emotion, but such editability is architecture- and trajectory-dependent and can be disrupted by...
| Authors | Tianxin Xie, Pengfei Zhang, Kai Jiang et al. |
|---|---|
| Primary category | cs.SD |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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