Speech Synthesis Enhancement via Neural Network Segmentation
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Solution Overview
Problem
Existing speech synthesis systems face challenges in producing natural-sounding speech due to amplitude quantization artifacts and limited computation resources, leading to reduced signal quality and perceptual degradation.
Innovation Solution
A speech synthesis system that employs a two-part process involving a synthesis network and an enhancement network, where the synthesis network generates an intermediate waveform with fewer quantization levels and a lower sampling rate, and the enhancement network transforms these samples into enhanced time samples with improved quality, using a parameterized non-linear transformation and trained neural networks to reduce quantization noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If waveform sample values are quantized to fewer levels (e.g., 256 levels of μ-law non-uniform division), then computational resources and memory requirements are reduced, but amplitude quantization artifacts and distortion increase, degrading speech quality
Solution Approach 1:
The speech synthesis system is divided into two distinct networks: a synthesis network that generates quantized waveform samples efficiently, and an enhancement network that processes these samples to reduce quantization artifacts. This segmentation allows each component to be optimized for its specific function, resolving the contradiction between computational efficiency and signal quality.
Solution Approach 2:
The enhancement network acts as an intermediary between the quantized waveform output and the final speech signal. It processes the quantized samples through learned transformations to mitigate quantization artifacts before output, effectively mediating between the computational constraints and quality requirements.
2Manufacturing precision
If a neural network processes phonetic labels to produce waveform sample values directly at high quality, then speech naturalness is improved, but computational resources and processing time are excessively consumed
Solution Approach 1:
The processing task is segmented into two stages: first generating quantized waveform samples with lower computational cost, then enhancing them to achieve naturalness. This avoids the need for a single high-cost network to perform both generation and quality enhancement simultaneously.
Solution Approach 2:
The synthesis network intentionally produces quantized samples with fewer quality characteristics (partial action), accepting degraded quality as an intermediate state. The enhancement network then applies targeted improvements to achieve the desired naturalness without requiring the synthesis network to compute full-quality waveforms from the start.
3Measurement precision
If the sampling rate and quantization levels are increased to improve audio bandwidth and quality, then perceptual quality is enhanced, but the complexity and resource requirements of the synthesis system increase
Solution Approach 1:
The system separates the functions of waveform generation and quality enhancement into distinct networks. The synthesis network operates at lower resolution (fewer quantization levels and sampling rates), while the enhancement network restores quality characteristics, avoiding the need for the entire system to operate at high complexity simultaneously.
Solution Approach 2:
The enhancement network learns parameterized transformations that effectively increase the perceived quantization levels and sampling rate quality without actually increasing the raw data resolution. It applies learned adjustments to the quantized samples to achieve higher perceptual quality at lower computational parameters.
Data Source
AI summary
An approach to speech synthesis uses two phases in which a relatively low quality waveform is computed, and that waveform is passed through an enhancement phase which generates the waveform that is ultimately used to produce the acoustic signal provided to the user. For example, the first phase and the second phase are each implemented using a separate artificial neural network. The two phases may be computationally preferable to using a direct approach to yield a synthesized waveform of comparable quality.