Expressive Speech Synthesis via Style Encoding and Segmentation
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Solution Overview
Problem
Conventional text-to-speech systems produce unrealistic, artificial sounding speech and lack fine-grained control over expressivity, often requiring large, complex models for training.
Innovation Solution
A system using a machine-learned synthesizer with a text encoder, speech style encoder, and decoder to generate expressive speech audio from user-input text and selected speech style features, allowing for fine-grained control over speech attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional text-to-speech systems are used, then the system structure is simple, but the speech output sounds unrealistic and artificial
Solution Approach 1:
The system segments the speech synthesis process into distinct functional modules: text encoder, speech style encoder, combiner, and vocoder. Each module handles a specific aspect of speech generation, allowing the system to achieve realistic output through coordinated processing of multiple independent components rather than a single complex black box
Solution Approach 2:
The patent introduces a combiner module as an intermediary that integrates text encodings and speech style encodings before the vocoder processes them. This intermediary layer mediates between the textual content and stylistic requirements, enabling the system to reconcile potentially conflicting objectives and produce realistic speech that satisfies both constraints
2Adaptability or versatility
If expressive text-to-speech systems are used, then the speech expressivity is improved, but the model size and training requirements increase significantly
Solution Approach 1:
The system applies local quality by separately encoding speech style features as distinct from text content, allowing fine-grained control over expressive attributes without requiring the model to learn all possible expressions from scratch. The speech style encoder captures localized stylistic information that can be applied to any text input, reducing the need for extensive diverse training data
Solution Approach 2:
The patent utilizes parameter changes by representing speech style through a set of learnable parameters (speech style encoding) that can be adjusted to produce different expressive characteristics. This parameter-based approach allows the system to generate varied speech styles by modifying the encoding parameters rather than requiring proportional increases in training data for each new style
3Measurement precision
If high-dimensional speech style features are used, then the speech style representation is more accurate, but the computational complexity and storage requirements increase
Solution Approach 1:
The system extracts only the essential speech style information into a compressed representation (speech style encoding) that captures the necessary stylistic characteristics while discarding redundant information. This extraction process reduces the dimensionality of the feature space, maintaining representation accuracy while significantly reducing computational and storage requirements
Data Source
AI summary
A system for use in video game development to generate expressive speech audio comprises a user interface configured to receive user-input text data and a user selection of a speech style. The system includes a machine-learned synthesizer comprising a text encoder, a speech style encoder and a decoder. The machine-learned synthesizer is configured to generate one or more text encodings derived from the user-input text data, using the text encoder of the machine-learned synthesizer; generate a speech style encoding by processing a set of speech style features associated with the selected speech style using the speech style encoder of the machine-learned synthesizer; combine the one or more text encodings and the speech style encoding to generate one or more combined encodings; and decode the one or more combined encodings with the decoder of the machine-learned synthesizer to generate predicted acoustic features. The system includes one or more modules configured to process the predicted acoustic features, the one or more modules comprising a machine-learned vocoder configured to generate a waveform of the expressive speech audio.