arXiv:2606.09048v1 Announce Type: cross Abstract: 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, diff
The paper leverages recent advancements in flow-matching models and the desire for more efficient and direct generative AI architectures, particularly for high-fidelity audio synthesis.
This development pushes the frontier of text-to-speech by eliminating intermediate steps, potentially leading to more natural, expressive, and efficient voice generation critical for human-computer interaction and content creation.
The shift to 'waveform-native' direct text-to-wave generation without intermediate acoustic representations simplifies TTS system architectures and could improve output quality and reduce computational overhead.
- · AI voice generation companies
- · Content creators
- · Gaming industry
- · Accessibility technology developers
- · Legacy TTS providers with complex multi-stage pipelines
- · Companies reliant on intermediate acoustic models
Higher quality and more natural synthetic voices become pervasive in digital interfaces and media.
Reduced latency and computational costs for real-time AI voice applications, expanding their deployment.
Enhanced realism blurs the line between human and synthetic speech, requiring new authentication and detection methods.
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Read at arXiv cs.AI