Speech Synthesis Model Using Prosody Features for Real-Time Vocoding
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Solution Overview
Problem
Current speech synthesis technologies face challenges in achieving high real-time rates, which affect user experience in terms of fluency and sound quality.
Innovation Solution
A speech synthesis method that involves acquiring to-be-synthesized text, extracting prosody features, inputting the text and prosody features into a speech synthesis model to obtain a vocoder feature, and then using this feature in a vocoder to generate synthesized speech, with optional post-processing steps like linear interpolation and UV correction to enhance sound quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional speech synthesis methods are used, then speech can be generated, but the real-time rate is low affecting fluency and user experience
Solution Approach 1:
The speech synthesis process is segmented into distinct modules: text processing module, prosody analysis module, speech synthesis model, and vocoder. Each module handles specific tasks independently, allowing for optimized processing pipelines that improve real-time performance while maintaining quality through specialized processing at each stage.
Solution Approach 2:
The system extracts and utilizes prosody features (pitch, energy, duration) as key parameters to control the synthesis process. By explicitly modeling and adjusting these parameters through the speech synthesis model and vocoder, the system achieves both high real-time rate and maintained sound quality through parameter-optimized generation.
2Reliability
If complex speech synthesis models are used, then sound quality improves, but computational requirements increase
Solution Approach 1:
The complex synthesis task is divided into manageable segments handled by specialized models: text processing for semantic understanding, prosody analysis for rhythm and intonation, speech synthesis model for acoustic feature generation, and vocoder for waveform reconstruction. This segmentation allows each component to be computationally efficient while collectively achieving high quality.
Solution Approach 2:
The system introduces intermediate representations (prosody features, acoustic features) that bridge the gap between text input and speech output. These intermediates allow complex quality-preserving transformations to occur in structured steps rather than requiring a single computationally intensive model.
Data Source
AI summary
The present disclosure discloses a speech synthesis method and apparatus, a device and a computer storage medium, and relates to speech and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring to-be-synthesized text; acquiring a prosody feature extracted from the text; inputting the text and the prosody feature into a speech synthesis model to obtain a vocoder feature; and inputting the vocoder feature into a vocoder to obtain synthesized speech.