Language-Segmented Speech Synthesis for Multilingual Pronunciation
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Solution Overview
Problem
Current speech synthesis systems primarily support unilingual pronunciation, failing to achieve smooth multilingual pronunciation.
Innovation Solution
A method that segments text into different language categories, converts each segment into phonemes based on language-specific rules, and uses a speech synthesis model to generate vocoder parameters for accurate, natural speech synthesis, incorporating prosodic structures and tones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a speech synthesis system supports multiple languages, then the versatility and adaptability of the system improve, but the complexity of the system increases due to the need for language-specific processing rules and models
Solution Approach 1:
The patent segments the text input into different language categories first, then processes each segment separately through language-specific phoneme conversion rules. This segmentation approach allows the system to handle multiple languages by dividing the complex task into manageable language-specific sub-tasks, reducing the overall system complexity while maintaining multilingual versatility.
Solution Approach 2:
The patent implements a universal speech synthesis framework that can handle multiple languages through a common architecture. The system uses a unified text segmentation module and a shared phoneme conversion model that adapts to different languages, allowing one system to serve multiple language requirements without requiring completely separate processing systems for each language.
2Measurement precision
If the system processes text segment by segment according to language categories, then the accuracy of phoneme conversion improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary text segmentation into language categories before phoneme conversion. By pre-identifying the language type of each text segment, the system can prepare language-specific conversion rules and parameters in advance, which then enables more accurate and efficient phoneme conversion without requiring time-consuming language detection during the conversion process itself.
Data Source
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AI summary
The present disclosure relates to a speech synthesis method and device, and a computer-readable storage medium, and relates to the field of computer technology. The method of the present disclosure includes: dividing a text into a plurality of segments according to a language category to which each of the segments belongs; converting each of the segments into a phoneme corresponding to the segment to generate a phoneme sequence of the text according to the language category to which each of the segments belongs; inputting the phoneme sequence into a speech synthesis model trained in advance and converting the phoneme sequence into a vocoder characteristic parameter; and inputting the vocoder characteristic parameter into a vocoder to generate a speech.