Continuous Speech Synthesis using per-token Latent Diffusion
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.
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Arnon Turetzky
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Nimrod Shabtay
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2024-10-28T11:25:20.632Z
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Nimrod Shabtay
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NimrodShabtay1986
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Slava Shechtman
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2024-10-28T15:17:44.202Z
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Slava Shechtman
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slavashe
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Hagai Aronowitz
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2024-10-28T15:17:50.097Z
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Hagai Aronowitz
user
hagaia
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David Haws
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2024-10-28T15:17:55.258Z
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David Haws
user
dchaws
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Ron Hoory
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Avihu Dekel
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2024-10-22T07:55:44.222Z
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Avihu Dekel
user
Avihu
Continuous Speech Synthesis using per-token Latent Diffusion
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.
SALAD, a per-token latent diffusion model using continuous representations, achieves superior intelligibility in zero-shot text-to-speech without compromising speech quality and speaker similarity.
autoregressive transformer models latent diffusion model variable-length outputs diffusion head semantic tokens discrete speech synthesis techniques continuous speech modeling techniques intelligibility score speech quality speaker similarity