arXiv AI
Jun 8
dots.tts Technical Report
arXiv:2606. 07080v1 Announce Type: cross Abstract: We present dots.
DriftTTS: Few-Step Text-to-Speech Without Distillation via Distribution-Matching Drift
DriftTTS is a few‑step neural text‑to‑speech model that generates mel‑spectrograms without relying on a generative teacher, distillation, or adversarial discrimination. It employs a distribution‑matching drift objective in a mel‑domain feature space defined by raw mels and a frozen masked‑autoencoder encoder pretrained on LJSpeech. On the LJSpeech dataset, DriftTTS achieves competitive metrics (3.87 dB MCD, 3.7% WER) and a MOS of 4.18, rivaling the Matcha‑TTS baseline and approaching ground‑truth quality.
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