Cauchy Diffusion Speech Synthesis for Robust Audio Generation
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Solution Overview
Problem
Existing denoising probabilistic diffusion models for speech synthesis primarily use Gaussian noise, which limits their performance, as Cauchy noise, being more robust, cannot be directly applied due to the core assumption of Gaussian distributions in these models.
Innovation Solution
Introduce Cauchy noise into denoising probabilistic diffusion models by defining Cauchy noise and posterior square scale tables, implementing Cauchy diffusion operations, and constructing a denoising neural network with specific loss functions to train and sample for speech synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Gaussian noise is used in denoising probabilistic diffusion models for speech synthesis, then the model can be trained and sampled effectively, but the robustness and quality of synthesized speech are limited
Solution Approach 1:
The patent changes the noise distribution parameter from Gaussian to Cauchy, which has heavier tails and is more robust to outliers. This parameter change allows the model to handle noisy speech data more effectively, improving robustness and quality of synthesized speech while maintaining the diffusion model framework through appropriate mathematical adjustments.
2Reliability
If Cauchy noise is directly applied to denoising probabilistic diffusion models, then robustness is improved, but the model cannot satisfy the Kolmogorov equation and core Gaussian distribution assumptions
Solution Approach 1:
The patent introduces an intermediary transformation approach where Cauchy noise is converted to a form compatible with the diffusion model framework. By using the relationship between Cauchy and Gaussian distributions through scaling transformations, the model can utilize Cauchy noise's robustness properties while maintaining the mathematical assumptions required for diffusion process formulation.
3Ease of manufacture
If traditional Gaussian-based diffusion models are used, then the training process is simple, but the diversity and quality of synthesized speech are insufficient
Solution Approach 1:
The patent modifies the noise distribution parameter from Gaussian to Cauchy, which provides heavier tails and better robustness to outliers in speech data. This parameter change enhances the diversity and quality of synthesized speech by allowing the model to learn from a broader range of acoustic patterns while maintaining a relatively simple training framework.
Data Source
AI summary
The present invention discloses a speech synthesis method and device based on Cauchy denoising probabilistic diffusion models, comprising: (1) calculating a Cauchy noise table for speech synthesis; (2) calculating a Cauchy posterior square scale table for speech synthesis; (3) implementing a Cauchy diffusion process for speech synthesis; (4) calculating the loss function of Cauchy denoising neural network for speech synthesis; (5) implementing the sampling process of Cauchy denoising diffusion models for speech synthesis. The present invention introduces Cauchy noise into the denoising probabilistic diffusion models, achieves model training and sampling, and ultimately completes speech synthesis. The present invention can improve the robustness of the speech synthesis method and significantly enhance the quality of synthesized speech.