Speech Synthesis Model Training for Style-Timbre Generalization
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Solution Overview
Problem
Current speech synthesis models require separate training for each style label or prompt text, leading to high costs and poor generalization on new styles, necessitating a more efficient and effective training method.
Innovation Solution
A method for training a speech synthesis model that combines style and timbre sample speeches with input text to generate output samples, using a semantic encoding network and decoding network trained on these samples to improve generalization and reduce training costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate training is performed for each style label or prompt text, then the model can achieve accurate style-specific synthesis, but the training cost increases and generalization to new styles deteriorates
Solution Approach 1:
The patent merges multiple style-specific training tasks into a single unified training process. Instead of training separate models for each style label, the system combines style labels, prompt texts, and their corresponding speech samples into a single training dataset, allowing one model to learn and generalize across multiple styles simultaneously. This resolves the contradiction by maintaining style-specific accuracy through comprehensive data representation while dramatically improving training efficiency through consolidation.
Solution Approach 2:
The patent creates a universal speech synthesis model that can handle multiple style labels and prompt text types through a single training process. The model learns to generalize from diverse input formats (style labels, prompt texts) and their corresponding speech samples, enabling it to perform style-specific synthesis without requiring separate specialized training for each style. This multi-functionality approach maintains reliability for specific styles while improving overall productivity.
2Reliability
If separate training is performed for each style label or prompt text, then the model can be specialized for specific styles, but the training cost and time consumption increase
Solution Approach 1:
The patent combines multiple style-specific training processes into a single unified training operation. By aggregating style labels, prompt texts, and their corresponding speech samples into one comprehensive training dataset, the system reduces the total training time while maintaining the ability to generate accurate style-specific speech. This merging approach eliminates redundant training iterations and optimizes resource utilization.
Solution Approach 2:
The patent performs preliminary data preparation and feature extraction during the unified training process, pre-learning style characteristics and acoustic features that can be reused for subsequent style-specific synthesis tasks. This preliminary action reduces the need for time-consuming retraining when new styles are introduced, as the model can leverage previously learned representations and adapt quickly to new style requirements.
3Productivity
If a single unified training approach is used, then training efficiency improves, but the model's ability to capture and generalize style-specific features may deteriorate
Solution Approach 1:
The patent applies local quality by preserving and emphasizing style-specific information within the unified training framework. The system processes different input types (style labels, prompt texts) through dedicated processing paths that maintain their distinctive characteristics, while integrating them into a unified model structure. This allows the model to capture fine-grained style-specific features during unified training, preventing the loss of stylistic nuance while benefiting from improved training efficiency.
Solution Approach 2:
The patent introduces intermediary representation layers that bridge the gap between unified training data and style-specific output. These intermediary layers process and transform the unified training representations into style-conditioned features, enabling the model to generalize style-specific characteristics from the unified training process. The intermediary mechanisms ensure that style information is preserved and effectively transferred during the unified training approach.
4Device complexity
If style and timbre features are processed separately, then the processing pipeline is simple, but the feature fusion and generalization performance deteriorate
Solution Approach 1:
The patent merges style feature extraction and timbre feature processing into a unified feature fusion architecture. Instead of using separate independent pipelines, the system integrates multiple feature streams through shared computational graphs and unified representation spaces. This merging enables better interaction and fusion between style and timbre features, improving generalization performance while the integrated structure manages complexity through systematic organization.
Data Source
AI summary
A method for training a speech synthesis model includes obtaining training data; obtaining an initial speech synthesis model; training a semantic encoding network and a semantic decoding network in the speech synthesis model respectively based on a style sample speech, a timbre sample speech, an input sample text, and an output sample speech in training samples of the training data, to obtain a trained speech synthesis model.