Speech Synthesis Model Using Pause Feature Insertion
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Solution Overview
Problem
Current speech synthesis technologies face challenges in achieving fluent and natural speech generation, particularly in integrating prosodic information and handling multiple languages, which affects the overall quality and accuracy of synthesized speech.
Innovation Solution
The method involves determining phoneme and prosodic word boundary features, inserting pause characters to create a combined feature, and training a speech synthesis model using these features to incorporate prosodic information, thereby improving fluency and naturalness. This approach uses a neural network-based model and introduces pause hidden features through variational auto-encoders to enhance model performance and adaptability across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional speech synthesis methods are used, then the synthesis process is simple, but the fluency and naturalness of synthesized speech is poor
Solution Approach 1:
The patent segments prosodic information into distinct components (pause features, stress features, intonation features) that are processed separately and then integrated. The pause detector, stress detector, and intonation detector operate as independent modules, each extracting specific prosodic characteristics from the input signal, which are then combined to form comprehensive prosodic features for synthesis.
Solution Approach 2:
The patent introduces pause hidden features as intermediary representations that bridge the gap between raw audio signals and synthesized speech. These pause hidden features serve as mediators that capture essential prosodic information and transmit it through the synthesis model, enabling more natural pause insertion and overall fluency improvement.
2Reliability
If prosodic information is integrated into speech synthesis, then the naturalness of speech improves, but the processing complexity increases
Solution Approach 1:
The patent divides prosodic information processing into separate detection modules (pause detector, stress detector, intonation detector), each handling a specific aspect of prosody. This segmentation allows the system to process different prosodic features independently and then integrate them, reducing the complexity of any single processing stage while maintaining comprehensive prosodic coverage.
Solution Approach 2:
The patent performs preliminary extraction of prosodic features (pauses, stress, intonation) from the input signal before the main synthesis process. By pre-processing and organizing prosodic information in advance, the system prepares structured data that can be efficiently integrated during synthesis, reducing the computational burden during the critical speech generation phase.
3Reliability
If pause characters are inserted based on prosodic word boundaries, then speech fluency improves, but the feature processing complexity increases
Solution Approach 1:
The patent performs preliminary detection of prosodic word boundaries and insertion of pause characters before the main synthesis process. The pause detector identifies boundary positions in advance and inserts pause features into the feature sequence, preparing the data structure for subsequent synthesis operations and simplifying the overall processing pipeline.
Solution Approach 2:
The system uses the prosodic word boundary information to automatically determine where pause characters should be inserted, making the pause insertion process self-directed based on the inherent structure of the input signal. The prosodic features guide their own integration into the synthesis feature sequence without requiring external manual intervention.
Data Source
AI summary
The present disclosure discloses a method and apparatus for training a model, a method and apparatus for synthesizing a speech, a device and a storage medium, and relates to the field of natural language processing and deep learning technology. The method for training a model may include: determining a phoneme feature and a prosodic word boundary feature of sample text data; inserting a pause character into the phoneme feature according to the prosodic word boundary feature to obtain a combined feature of the sample text data; and training an initial speech synthesis model according to the combined feature of the sample text data, to obtain a target speech synthesis model.