Energy Score Training for Speech Synthesis Neural Networks
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Solution Overview
Problem
Existing speech synthesis systems, particularly those relying on adversarial training and autoregressive generative neural networks, face challenges with stability and computational resource efficiency, requiring many training epochs and consuming significant resources.
Innovation Solution
A feedforward generative neural network is trained using energy scores to generate output audio data efficiently, allowing for rapid convergence and reduced computational requirements, while maintaining high-quality output without explicitly modeling data distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If adversarial training with discriminators is used, then speech synthesis quality can be improved, but training stability deteriorates and training time increases
Solution Approach 1:
The patent extracts and removes the discriminator component from the adversarial training system, replacing it with an energy-based training objective. This eliminates the instability caused by training multiple networks in parallel while preserving the ability to generate high-quality speech through the energy score optimization approach.
Solution Approach 2:
The patent changes the training objective from adversarial loss to energy score-based loss. By defining an energy function that measures the likelihood of generated speech samples, the system transforms the training paradigm to achieve both high quality and stability through gradient-based optimization of the energy function rather than through adversarial game dynamics.
2Manufacturing precision
If adversarial training with discriminators is used, then speech synthesis quality can be improved, but training time increases
Solution Approach 1:
The patent removes the discriminator network and adversarial training loop, eliminating the computational overhead of training and coordinating multiple networks. The energy-based approach requires only training the generator network directly, significantly reducing training time while maintaining quality through the energy score optimization objective.
Solution Approach 2:
The patent uses parallel sampling of noise vectors to generate multiple speech samples simultaneously during training. This allows the model to learn from diverse samples in parallel without requiring sequential adversarial iterations, improving training efficiency while maintaining the quality benefits of generating multiple variations.
3Manufacturing precision
If autoregressive generative neural networks are used, then output quality can be maintained, but computational resources and generation time increase
Solution Approach 1:
The patent segments the speech generation process into parallel independent computations. Instead of generating speech samples sequentially as in autoregressive models, the energy-based model computes the energy score and generates samples in parallel, with each sample independent of the others, dramatically improving generation speed while maintaining quality.
Solution Approach 2:
The patent employs periodic batched sampling during inference, where multiple noise vectors are processed simultaneously to generate batches of speech samples. This parallel periodic generation maintains high output quality while achieving fast batched inference, contrasting with the sequential generation of autoregressive models.
4Manufacturing precision
If invertible feedforward neural networks are used, then realistic speech generation is achieved, but model complexity and training requirements increase
Solution Approach 1:
The patent extracts and removes the invertibility constraint and distillation requirements from the training process. The energy-based model achieves realistic speech generation through direct energy score optimization without requiring the complex invertible transformations or knowledge distillation steps needed by other feedforward approaches, simplifying both model architecture and training procedure.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a generative neural network to convert conditioning text inputs to audio outputs using energy scores.