BareWave: Waveform-Native Flow-Matching Text-to-Speech
Removing intermediate representations and separately trained decoding stages has become an important direction in generative modeling. In text-to-speech, however, high-quality systems are still commonly built through an intermediate acoustic representation before waveform synthesis. In this work, we present BareWave, a fully waveform-native framework for direct text-to-wave generation in flow-matching TTS. We consider this setting to raise three training challenges: raw-waveform modeling lacks a strong pretrained representational scaffold, different stages of training benefit from different noise schedules, and data-space perceptual objectives do not automatically share the temporal struc...
| Authors | Wei Fan, Chao-Hong Tan, Qian Chen et al. |
|---|---|
| Primary category | eess.AS |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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