Autoregressive Difference-Signal Speech Synthesis for Low-Power Audio
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech synthesis technologies are inefficient in terms of processor and memory usage, particularly in low-power devices and remote servers, making it difficult to generate high-quality audio waveforms under resource-constrained conditions.
Innovation Solution
Utilizing an autoregressive neural network model to generate audio waveforms by processing text, where the model generates difference signals representing changes between successive waveform samples, reducing the computational requirements and memory needed compared to generating full audio waveforms directly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full audio waveforms are generated directly using an autoregressive model, then audio quality is maintained, but processor usage and memory consumption increase significantly
Solution Approach 1:
The audio waveform generation process is segmented into two distinct stages: (1) generating a compact difference signal representation that captures essential audio characteristics, and (2) reconstructing the full audio waveform from these difference signals. This segmentation allows the computationally intensive autoregressive model to operate on smaller data structures, reducing processor usage while maintaining audio quality through the reconstruction stage.
Solution Approach 2:
Difference signals serve as an intermediary representation between the text input and the final audio waveform. Instead of directly generating full audio waveforms, the model first produces compressed difference signals that encode the essential temporal and spectral characteristics, which are then transformed into the final audio output. This intermediary step significantly reduces computational requirements.
2Measurement precision
If full audio waveforms are generated directly using an autoregressive model, then audio quality is maintained, but memory consumption increases
Solution Approach 1:
The audio data is segmented into compact difference signal representations that capture only the essential variations from one audio sample to the next. This segmentation reduces the memory footprint from storing complete high-resolution audio waveforms to storing only the differential changes, which require significantly less memory while preserving audio quality when reconstructed.
Solution Approach 2:
The model generates difference signals that represent only the necessary information to reconstruct audio quality, rather than generating complete audio data at each step. This partial action approach generates minimal sufficient data (difference signals) that can be expanded into full-quality audio through subsequent processing.
3Productivity
If difference signals are used instead of full audio waveforms, then processor efficiency improves, but computational complexity of the generation process increases
Solution Approach 1:
The essential characteristics of audio waveforms are extracted into compact difference signal representations. By taking out only the critical temporal and spectral information needed to represent audio quality and encoding it in a compressed differential form, the system reduces the data volume processed by the autoregressive model, improving processor efficiency while the extraction logic itself remains integrated within the model architecture.
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
Techniques are disclosed that enable generation of an audio waveform representing synthesized speech based on a difference signal determined using an autoregressive model. Various implementations include using a distribution of the difference signal values to represent sounds found in human speech with a higher level of granularity than sounds not frequently found in human speech. Additional or alternative implementations include using one or more speakers of a client device to render the generated audio waveform.