Continuous Speech Synthesis using per-token Latent Diffusion
Authors: name: Arnon Turetzky, name: Nimrod Shabtay, name: Slava Shechtman, name: Hagai Aronowitz, name: David Haws, name: Ron Hoory, name: Avihu Dekel

The success of autoregressive transformer models with discrete tokens has
inspired quantization-based approaches for continuous modalities, though these
often limit reconstruction quality. We therefore introduce SALAD, a per-token
latent diffusion model for zero-shot text-to-speech, that operates on
continuous representations. SALAD builds upon the recently proposed expressive
diffusion head for image generation, and extends it to generate variable-length
outputs. Our approach utilizes semantic tokens for providing contextual
information and determining the stopping condition. We suggest three continuous
variants for our method, extending popular discrete speech synthesis
techniques. Additionally, we implement discrete baselines for each variant and
conduct a comparative analysis of discrete versus continuous speech modeling
techniques. Our results demonstrate that both continuous and discrete
approaches are highly competent, and that SALAD achieves a superior
intelligibility score while obtaining speech quality and speaker similarity on
par with the ground-truth audio.