Neural Spline Normalizing Flows for Natural Speech Synthesis
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Solution Overview
Problem
Existing speech synthesis technologies struggle to accurately model natural variations in speech attributes such as intonation and volume, leading to robotic and unexpressive synthetic speech that fails to capture the nuances of human speech.
Innovation Solution
The use of neural networks with normalizing flows and non-linear splines to map low-level speech attributes like pitch and energy, incorporating techniques like U-shaped curve filling for unvoiced regions and multi-dimensional data grouping, to create more natural and expressive synthetic speech.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple mechanistic TTS synthesis is used, then the system is easy to implement and fast, but the speech sounds robotic and lacks natural variations
Solution Approach 1:
The patent replaces simple mechanistic TTS synthesis with neural network-based generative models that use normalizing flows and neural splines to model complex probability distributions of speech attributes. This substitution enables the system to capture natural variations in intonation, volume, and timing while maintaining computational efficiency through the normalizing flow transformation framework.
Solution Approach 2:
The patent transforms the modeling approach by changing from deterministic mechanical parameters to probabilistic neural parameters. The normalizing flow method models speech attributes (pitch, energy, timing) as samples from complex probability distributions rather than fixed mechanical rules, allowing the system to generate diverse and natural-sounding speech variations.
2Reliability
If complex neural network models are used to model speech attributes, then the quality and naturalness of synthetic speech improve, but the computational resources and system complexity increase
Solution Approach 1:
The patent segments the speech synthesis problem into separate attribute modeling tasks. Instead of modeling all speech characteristics simultaneously, the system independently models pitch, energy, and timing attributes using separate neural network components (flow networks and spline networks), making the overall complex system more manageable and efficient.
Solution Approach 2:
The patent introduces normalizing flows as an intermediary transformation layer between the neural network and the final speech generation. This intermediary component simplifies the relationship between complex neural network outputs and the target speech distributions by providing a structured transformation framework that reduces direct computational complexity.
3Stability of the object's composition
If deterministic speech synthesis is used, then the output is consistent and reproducible, but it fails to capture natural variations and expressiveness
Solution Approach 1:
The patent introduces dynamic probabilistic modeling to replace static deterministic synthesis. The normalizing flow framework models speech attributes as dynamic samples from probability distributions that can vary based on input text and speaker characteristics. This dynamic approach enables the system to generate diverse, natural variations while maintaining consistency through the learned distribution patterns.
Solution Approach 2:
The patent changes from fixed deterministic parameters to probabilistic parameters. By modeling speech attributes as samples from learned probability distributions rather than fixed values, the system can naturally vary outputs while maintaining statistical consistency. The neural splines and normalizing flows provide parameterized transformations that adapt to different speaking styles and contexts.
Data Source
AI summary
Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing generative text-to-speech models. The techniques include identifying a mapping of speech characteristics (SC) on a target distribution of a latent variable using a non-linear transformation for at least a subset of the SC. Parameters of the non-linear transformation are determined using a neural network that approximates a statistics of the SC with a statistics predicted for the SC based on the identified mapping and the target distribution of the latent variable.