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The Speech Generator Learned Without a Generative Teacher
DriftTTS trains a few-step mel-spectrogram generator through distribution matching and its own intermediate rollouts.
Summary
DriftTTS trains a few-step mel-spectrogram generator through distribution matching and its own intermediate rollouts.
DriftTTS avoids a pretrained generative teacher, distillation and adversarial discrimination. Its objective operates in a mel-domain feature space that includes a frozen masked-autoencoder trained on the same LJSpeech split. With four function evaluations, the authors report a 3.7 percent word-error rate versus Matcha-TTS’s 3.4 percent. A paired blind listening test gave mean scores of 4.18 and 3.96 respectively, with ground truth at 4.22. Those dataset-specific results support competitive synthesis under this setup, not universal voice quality or robustness.
Why it matters
DriftTTS trains a few-step mel-spectrogram generator through distribution matching and its own intermediate rollouts.
Limits and context
- Those dataset-specific results support competitive synthesis under this setup, not universal voice quality or robustness.
Key claims
DriftTTS trains a few-step mel-spectrogram generator through distribution matching and its own intermediate rollouts.
Qualification: Those dataset-specific results support competitive synthesis under this setup, not universal voice quality or robustness.
Evidence: source-2026-10-04-012
Sources
- arXiv preprint 2610.03390, original v1arXiv / submitting research authors · primary research
Corrections
: Publication note: This October 4 edition was reconstructed on October 5 after the workstation was offline. It uses original October 2 submissions from the standard Sunday-evening arXiv release, rather than claiming those papers were available for the missed 9 AM run. The date and 9 AM publication slot preserve archive chronology; October 5 developments are excluded. The September 27 Invention Desk is carried over because its replacement was generated October 5.