Style-Conditioned Text-to-Speech with Disentangled Audio Control
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Solution Overview
Problem
Existing AI speech generation systems struggle to produce speech with human-understandable and controllable style and environmental characteristics, such as emotional content and acoustic environments, which are crucial for realistic and efficient audio production.
Innovation Solution
A style model is trained to encode style information into a style vector, using techniques like variational autoencoders and disentanglement strategies to separate emotional and environmental characteristics into distinct regions, allowing human control and understanding of generated speech.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional AI speech generation systems are used, then speech can be generated, but the style and environmental characteristics (emotional content, acoustic environments) are not human-understandable or controllable
Solution Approach 1:
The patent segments style information into distinct regions within the style vector, separating emotional characteristics from environmental characteristics. This segmentation allows human operators to control specific style attributes independently while maintaining human-understandable mappings between vector regions and style concepts.
Solution Approach 2:
The patent applies local quality by assigning different semantic meanings to different regions of the style vector. Specific regions encode specific style characteristics (e.g., emotional content in one region, environmental acoustics in another), enabling targeted control and interpretation of style information without affecting other attributes.
2Adaptability or versatility
If style information is encoded into a unified vector, then all style characteristics are captured, but individual characteristics cannot be separately controlled or understood
Solution Approach 1:
The style vector is segmented into multiple functional regions, each responsible for encoding specific style characteristics. This allows the system to maintain a unified vector structure for comprehensive style representation while enabling independent control and manipulation of individual style attributes through region-specific operations.
Solution Approach 2:
The patent introduces a structural dimension to the style vector by organizing it into distinct regions with specific semantic meanings. This dimensional organization transforms a flat vector into a structured representation where position and region encode additional information about style characteristics, enabling both completeness and controllability.
Data Source
AI summary
A style encoder can be trained to encode audio style and audio characteristics into selected regions of a style vector. The style vector can be used to condition a text to speech (TTS) model to generate speech with human-understandable and controllable styles. Various training strategies of the style encoder are described, including a first, second and third training strategy that can be used to disentangle audio styles into selected regions of a style vector. The distinct regions of the style vector can be used to provide numerous customization options to a user of the described system, along with tools to generate speech with a speaker identity and using selected audio styles and characteristics.