Neural Network Prosody Control for TTS
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Solution Overview
Problem
Current text-to-speech (TTS) systems have limited control over prosody, generating speech prosody implicitly and lacking explicit control over speaking style, emotional state, speaking rate, and expressiveness, with existing methods being either expensive, error-prone, or resulting in inconsistent synthesized speech quality.
Innovation Solution
A system that uses a neural network to predict acoustic sequences by generating combined prosody info based on linguistic sequences and corresponding acoustic sequences, allowing for explicit control of prosody through a processor that receives training targets and generates observed prosody info, enabling sentence-wise speaking pace and expressiveness control on a continuous scale.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current TTS systems generate speech prosody implicitly, then the system complexity is reduced, but the control over speaking style, emotional state, speaking rate, and expressiveness is limited
Solution Approach 1:
The patent segments prosody control into multiple independent dimensions including speaking style, emotional state, speaking rate, and expressiveness. Each dimension is controlled by separate neural network components that process specific prosodic features, allowing independent adjustment of each prosodic aspect without increasing overall system complexity
Solution Approach 2:
The patent introduces prosody embeddings as intermediary representations that bridge the gap between text input and acoustic output. These embeddings serve as intermediate variables that encode prosodic information and can be manipulated to control speaking style, emotional state, and other prosodic parameters without directly modifying the core TTS architecture
2Measurement precision
If human labeling is used for prosodic control, then the accuracy of prosody control is improved, but the cost and time consumption increase significantly
Solution Approach 1:
The patent implements self-service prosody extraction by training neural networks to automatically derive prosodic features directly from raw audio recordings and corresponding text. The system performs its own prosody annotation by learning to extract speaking style, emotional state, and other prosodic parameters without requiring external human labelers, thereby eliminating labeling time and cost
Solution Approach 2:
The patent replaces the mechanical process of human labeling with automated neural network-based prosody extraction. The system uses machine learning models to substitute human annotators, achieving accurate prosody control through automated feature extraction from audio signals while eliminating the time-consuming and expensive human labeling process
3Reliability
If exemplar-based prosody control is used, then the speaking style transfer is improved, but the feasibility in real TTS applications decreases due to resource constraints
Solution Approach 1:
The patent transforms the exemplar-based approach into a parameter-based control system by representing speaking styles as continuous embedding vectors rather than discrete exemplars. This allows smooth interpolation between different speaking styles and enables fine-grained control through parameter adjustment, making the system more adaptable and easier to implement in real-world scenarios
Solution Approach 2:
The patent creates a universal prosody control framework that can handle multiple functions including speaking style transfer, emotional state control, and speaking rate adjustment through a single neural network architecture. This multi-functional approach eliminates the need for separate exemplar databases for each function, reducing resource requirements and improving implementation feasibility across diverse TTS applications
Data Source
AI summary
An example system includes a processor to receive training targets. The training targets include an observed prosody info vector. The processor can train a neural network to predict acoustic sequences based on the training targets. The processor can train a prosody info generator to predict combined prosody info.