Feedforward Audio Generation from Unaligned Text Inputs
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Solution Overview
Problem
Existing speech synthesis systems require aligned conditioning text inputs and ground-truth audio outputs, necessitating large, hand-curated training datasets, and rely on computationally expensive autoregressive neural networks, leading to inefficiencies in training and resource consumption.
Innovation Solution
A feedforward generative neural network system that uses an alignment neural network to predict temporal alignment between text inputs and audio outputs, combined with discriminators for adversarial training, allowing for faster and more efficient generation of realistic audio samples without explicit data distribution modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If aligned conditioning text inputs and ground-truth audio outputs are used for training, then the speech synthesis system can be trained effectively, but large hand-curated training datasets are required
Solution Approach 1:
The alignment neural network pre-processes the unaligned text and audio inputs to predict temporal alignment and generate aligned conditioning sequences before the main synthesis process. This preliminary alignment step enables effective training without requiring manually curated aligned datasets, resolving the contradiction by automating the alignment preparation.
2Reliability
If autoregressive neural networks are used for speech synthesis, then realistic audio output can be generated, but computational resources and training time are excessively consumed
Solution Approach 1:
The speech synthesis process is divided into distinct segments: an alignment neural network that predicts temporal alignment, a feedforward generative network that produces audio features, and an audio synthesis component that generates the final waveform. This segmentation allows parallel processing and eliminates the sequential autoregressive generation, dramatically improving speed while maintaining realism through the adversarial training framework.
Solution Approach 2:
The traditional autoregressive mechanical generation process is replaced with a feedforward neural network approach combined with adversarial training. Instead of generating audio samples sequentially one at a time, the system processes entire sequences in parallel through the feedforward network, with the discriminator providing feedback to ensure realism, thus substituting the slow mechanical autoregressive process with a faster neural approach.
3Device complexity
If unaligned text inputs are processed directly by the generative network, then the system architecture is simplified, but temporal alignment between text and audio cannot be established
Solution Approach 1:
The alignment neural network serves as an intermediary component between the unaligned text inputs and the feedforward generative network. It processes the text inputs to predict temporal alignment and generates aligned conditioning sequences that preserve the temporal relationships needed for accurate speech synthesis, while keeping the overall architecture relatively simple through this dedicated intermediate layer.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a generative neural network to convert conditioning text inputs to audio outputs. The generative neural network includes an alignment neural network that is configured to receive a generative input that includes the conditioning text input and to process the generative input to generate an aligned conditioning sequence that comprises a respective feature representation at each of a plurality of first time steps and that is temporally aligned with the audio output.