Feedforward Speech Synthesis With Conditional Adversarial Discriminators
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Solution Overview
Problem
Existing audio generation techniques, such as autoregressive neural networks, are computationally intensive and require significant resources, while invertible feedforward networks need distillation to model data distributions, limiting their efficiency and realism.
Innovation Solution
A feedforward generative neural network trained adversarially with conditional and unconditional discriminators, using dilated convolutional layers to learn audio dependencies, generates high-quality audio in a single pass without explicit data distribution modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If autoregressive neural networks are used for audio generation, then audio quality can be maintained, but computational resources and time consumption increase significantly
Solution Approach 1:
The patent inverts the traditional autoregressive approach by using a feedforward architecture that generates audio samples in parallel rather than sequentially. Instead of generating each sample based on previous samples, the model generates all samples simultaneously from the input text, fundamentally reversing the generation paradigm to achieve both high quality and fast speed
Solution Approach 2:
The patent segments the audio generation task into multiple parallel processing streams using a U-Net architecture with skip connections. The encoder processes the input text independently, the decoder generates audio features in parallel, and skip connections preserve temporal information, enabling efficient parallel computation while maintaining audio quality
2Productivity
If invertible feedforward neural networks are used, then generation speed improves, but they require distillation and explicit data distribution modeling which increases complexity
Solution Approach 1:
The patent introduces a discriminator network as an intermediary that guides the feedforward generator without requiring explicit data distribution modeling. The discriminator provides adversarial feedback that implicitly captures the data distribution, eliminating the need for complex distillation procedures while maintaining generation quality
Solution Approach 2:
The patent implements adversarial feedback through the discriminator network that evaluates generated audio samples and provides gradient signals to improve the generator. This feedback mechanism enables the model to learn the audio data distribution implicitly through competition rather than explicit modeling, reducing overall system complexity
3Loss of time
If feedforward generative neural networks are used, then generation time is reduced, but audio realism may be compromised without proper training methods
Solution Approach 1:
The patent employs dynamic training where the discriminator and generator are trained alternately in an adversarial manner. The discriminator becomes increasingly sophisticated at detecting fake audio, which forces the generator to continuously improve its realism, creating a dynamic improvement process that maintains high audio quality throughout training
Solution Approach 2:
The patent uses dilated convolutional layers with varying dilation rates to capture audio dependencies at multiple temporal scales. This parameter variation allows the model to learn both fine-grained and coarse-grained audio patterns, ensuring realistic audio generation while maintaining the efficiency of feedforward processing
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output audio examples using a generative neural network. One of the methods includes obtaining a training conditioning text input; processing a training generative input comprising the training conditioning text input using a feedforward generative neural network to generate a training audio output; processing the training audio output using each of a plurality of discriminators, wherein the plurality of discriminators comprises one or more conditional discriminators and one or more unconditional discriminators; determining a first combined prediction by combining the respective predictions of the plurality of discriminators; and determining an update to current values of a plurality of generative parameters of the feedforward generative neural network to increase a first error in the first combined prediction.