F0 Transfer Learning for Real-Time Speech Synthesis
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Solution Overview
Problem
Current text-to-speech systems face challenges in producing natural and expressive synthesized speech due to discontinuous prosody and long-term structure mismatch, particularly with large neural networks that are memory-intensive and time-consuming for real-time applications.
Innovation Solution
F0 transfer learning from larger deep neural network models to smaller models, using LSTM and MLP feed-forward hidden layers, where fundamental frequency values are extracted and applied to train a smaller model for real-time speech synthesis, reducing complexity and memory requirements while maintaining high-quality output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If larger deep neural network models are used for F0 prediction, then speech synthesis quality is improved, but memory consumption and computational time increase
Solution Approach 1:
The patent extracts fundamental frequency (F0) values from a larger pre-trained model and uses only these extracted values for training a smaller model, rather than using the entire large model structure. This extraction approach maintains speech synthesis quality while reducing model complexity and memory requirements for real-time application.
Solution Approach 2:
The patent segments the model training process into two phases: first training a larger model to capture complex speech patterns, then creating a separate smaller model that uses only the F0 predictions from the larger model as input features. This segmentation allows the smaller model to focus on specific aspects of speech synthesis without requiring the full complexity of the larger model.
2Measurement precision
If larger deep neural network models are used for F0 prediction, then speech synthesis quality is improved, but real-time processing speed deteriorates
Solution Approach 1:
The patent extracts only the essential F0 prediction capability from a larger model and applies it to a smaller model, removing unnecessary computational complexity. This extraction enables real-time processing while preserving the quality benefits of the larger model's F0 predictions.
Solution Approach 2:
The patent performs preliminary training of a larger model offline to extract F0 values, which are then used to train a smaller model for real-time deployment. This preliminary action separates the quality-enhancing training phase from the speed-critical inference phase, allowing the smaller model to achieve real-time performance.
3Measurement precision
If larger deep neural network models are used for F0 prediction, then prediction accuracy is improved, but memory requirements increase
Solution Approach 1:
The patent extracts F0 prediction accuracy from a larger model without extracting its memory-intensive structure. By using only the extracted F0 values as training data for a smaller model, the system achieves high prediction accuracy with minimal memory consumption suitable for real-time applications.
Solution Approach 2:
The patent creates a simplified copy of the larger model's F0 prediction capability rather than using the full model. The smaller model copies only the essential F0 prediction function, achieving similar accuracy with significantly reduced memory requirements through transfer learning.
Data Source
AI summary
A system and method are presented for F0 transfer learning for improving F0 prediction with deep neural network models. Larger models are trained using long short-term memory (LSTM) and multi-layer perceptron (MLP) feed-forward hidden layer modeling. The fundamental frequency values for voiced and unvoiced segments are identified and extracted from the larger models. The values for voiced regions are transferred and applied to training a smaller model and the smaller model is applied in the text to speech system for real-time speech synthesis output.