Multilingual Text Tokenization for Unified Feature Extraction
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Solution Overview
Problem
Existing speech synthesis systems require separate speech synthesis front-ends for each language, leading to increased resource occupation and inefficient use of online resources.
Innovation Solution
A method for text analysis that converts text into token sequences of a uniform type, allowing for language-independent feature extraction and processing, using a multilingual vocabulary and a pre-trained feature extraction unit for zero-shot learning across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate speech synthesis front-ends are built for each language, then language-specific text analysis accuracy is improved, but online resource occupation increases
Solution Approach 1:
The patent applies universality by designing a single speech synthesis front-end that can process multiple languages. The core innovation lies in using language-independent tokenization and feature extraction mechanisms that work across different languages without requiring separate front-end systems for each language, thereby reducing online resource occupation while maintaining text analysis accuracy.
Solution Approach 2:
The patent merges multiple language-specific processing capabilities into a unified front-end system. By combining language-agnostic tokenization, universal feature extraction, and multi-language phoneme generation into a single system, it eliminates the need for separate front-ends for each language, thus reducing resource consumption while preserving accuracy.
2Adaptability or versatility
If separate speech synthesis front-ends are built for each language, then language-specific processing capability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal front-end architecture that handles multiple languages through a single system. The language-independent tokenization and feature extraction components provide adaptability across languages without requiring separate processing pipelines, thereby maintaining versatility while reducing system complexity.
Solution Approach 2:
The patent introduces language-independent tokens and universal feature representations as intermediaries between the input text and the phoneme generation stage. These intermediaries enable the system to process different languages uniformly without requiring language-specific processing logic, thus reducing system complexity while maintaining language adaptability.
3Measurement precision
If language-specific feature extraction is performed, then extraction precision for each language is improved, but processing time increases
Solution Approach 1:
The patent employs a universal feature extraction mechanism that operates independently of language. By extracting features from language-independent tokens, the system achieves consistent extraction precision across multiple languages without requiring separate extraction processes for each language, thereby reducing total processing time.
Solution Approach 2:
The patent segments the text processing into language-independent stages: tokenization, feature extraction, and phoneme generation. By separating the feature extraction stage from language-specific considerations and operating on universal tokens, it achieves efficient processing that maintains precision while reducing time loss.
Data Source
AI summary
Provided are an electronic device and a computer readable storage medium. The method includes: acquiring a text to be analyzed; performing token conversion on words in the text to be analyzed to obtain a token sequence to be analyzed, where tokens in token sequences to be analyzed corresponding to texts to be analyzed in different languages belong to a same type; and performing feature extraction on the token sequence to be analyzed, and processing a target task based on the extracted feature, to determine an analysis result for the text to be analyzed.