Speech Synthesis From Continuous Features Using Per-Token Latent Diffusion
Audio and Speech Processing
2025-11-25 v2
Abstract
We present SALAD, a zero-shot TTS autoregressive model operating over continuous speech representations. SALAD utilizes a per-token diffusion process to refine and predict continuous representations for the next time step. We compare our approach against a discrete variant of SALAD as well as publicly available zero-shot TTS systems, and conduct a comprehensive analysis of discrete versus continuous modeling techniques. Our results show that SALAD achieves superior intelligibility while matching the speech quality and speaker similarity of ground-truth audio.
Cite
@article{arxiv.2410.16048,
title = {Speech Synthesis From Continuous Features Using Per-Token Latent Diffusion},
author = {Arnon Turetzky and Avihu Dekel and Nimrod Shabtay and Slava Shechtman and David Haws and Hagai Aronowitz and Ron Hoory and Yossi Adi},
journal= {arXiv preprint arXiv:2410.16048},
year = {2025}
}
Comments
ASRU 2025