Harmonizing Spectral Evolution in Conditional Flow Matching for TTS
Read the original on arXiv Machine Learning →Conditional Flow Matching models for text‑to‑speech often produce incoherent frequency evolution during inference. The authors propose a training‑free, frequency‑selective boosting strategy that uses the Discrete Wavelet Transform to dynamically modulate mel‑spectrogram sub‑bands during ODE integration, penalizing aggressive low‑frequency growth while boosting lagging high‑frequency details. Across multiple architectures, this method reduces the number of function evaluations from 32 to 26 and improves Frechet Audio Distance by up to 61% without harming mean opinion scores, speaker similarity, or intelligibility.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.