Speech Style Transfer Neural Network Architecture
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Solution Overview
Problem
Current speech style transfer methods face challenges in realistically converting the speech of one person to mimic another, especially when they speak different languages, and struggle to produce intelligible and natural-sounding speech with accurate emotional conveyance and handling multiple target speakers.
Innovation Solution
The implementation of a computer-implemented method using neural networks, including bi-directional recurrent neural networks and a hierarchy of modules operating at different temporal resolutions, for training a speech synthesizer that receives and processes phoneme sequences and pitch contour data to generate synthesized audio signals, allowing for speech style transfer across languages and age ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional speech style transfer methods are used, then the system structure is simple, but the speech generation is unintelligible and unnatural
Solution Approach 1:
The speech generation system is divided into multiple specialized modules: a style encoder that extracts target speaker characteristics, a condition encoder that processes source speaker input, and a generator that synthesizes the final audio. This segmentation allows each module to focus on specific aspects of speech synthesis, improving overall intelligibility and naturalness while managing complexity through functional decomposition.
Solution Approach 2:
The patent introduces intermediate representations including style codes that capture target speaker characteristics and condition codes that process source speaker input. These intermediate representations act as mediators between the style encoder and generator, enabling the system to transfer speech styles across different speakers and languages while maintaining intelligibility and naturalness.
2Reliability
If the system processes speech at high temporal resolution, then the speech sounds more natural, but the processing time increases
Solution Approach 1:
The system dynamically adjusts the temporal resolution of processing based on the requirements of different speech segments and styles. The hierarchical module structure allows the system to process speech at varying rates, maintaining naturalness in critical regions while reducing processing time in less critical areas, thus balancing quality and efficiency.
Solution Approach 2:
The style encoder performs preliminary extraction of target speaker characteristics before the main generation process. By pre-computing style codes and storing them in a database, the system avoids redundant processing during actual speech generation, significantly reducing training and processing time while maintaining high temporal resolution for naturalness.
3Adaptability or versatility
If the system is trained on diverse speakers and languages, then the adaptability improves, but the training data requirements increase
Solution Approach 1:
The style encoder is designed to universally extract speaker characteristics from diverse speakers and languages, creating a multi-functional system that can handle various speech synthesis tasks. The generator is trained to accept different style codes and condition codes combinations, enabling it to produce natural-sounding speech across multiple languages and speakers without requiring separate models for each.
Solution Approach 2:
The system changes the parameter representation approach by converting diverse speaker and language data into standardized style codes and condition codes. This parameter transformation allows the system to handle diverse training data in a unified manner, improving adaptability across languages and speakers while managing training data requirements through efficient representation learning.
Data Source
AI summary
Computer-implemented methods for speech synthesis are provided. A speech synthesizer may be trained to generate synthesized audio data that corresponds to words uttered by a source speaker according to speech characteristics of a target speaker. The speech synthesizer may be trained by time-stamped phoneme sequences, pitch contour data and speaker identification data. The speech synthesizer may include a voice modeling neural network and a conditioning neural network.