Character-to-Phoneme Conversion for Polyphone and Tone Disambiguation
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Solution Overview
Problem
Existing grapheme-to-phoneme conversion systems for Mandarin text struggle with polyphones and tone changes, requiring preprocessing for linguistic features and large amounts of annotated data, making them difficult to scale and costly.
Innovation Solution
A unified neural network model integrates knowledge from rule-based and model-based systems using a knowledge-transfer character-to-phone pipeline, leveraging unlabeled data to perform end-to-end character-to-phoneme conversion without additional preprocessing, utilizing a modified BERT model for faster performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based systems with preprocessing are used for character-to-pinyin conversion, then polyphone disambiguation accuracy is improved, but system complexity and annotation costs increase
Solution Approach 1:
The patent merges multiple separate processing components (preprocessing module, polyphone disambiguation module, tone change handling module) into a unified neural network model. This single model performs all functions end-to-end without requiring separate preprocessing steps, thereby reducing system complexity while maintaining conversion accuracy.
Solution Approach 2:
The neural network model is designed to handle multiple tasks simultaneously: character-to-pinyin conversion, polyphone disambiguation, and tone change detection within a single unified framework. This multi-functional approach eliminates the need for separate specialized modules for each task.
2Measurement precision
If supervised models with large amounts of annotated data are used, then conversion accuracy is improved, but annotation costs and data acquisition time increase
Solution Approach 1:
The system performs preliminary processing by generating pseudo-labeled data through the neural network's initial predictions, which are then used to refine the model. This preliminary action reduces the need for extensive manually annotated training data while still achieving high conversion accuracy.
3Measurement precision
If traditional pipeline systems with preprocessing are used, then handling of tone changes is improved, but processing speed and scalability decrease
Solution Approach 1:
The patent combines tone change detection and character-to-pinyin conversion into a single neural network forward pass. This eliminates the sequential processing required in traditional pipelines, significantly improving processing speed while maintaining the ability to accurately detect and handle tone changes through contextual understanding.
Data Source
AI summary
Systems and methods for training a model to perform end-to-end character-to-phoneme (C2P) conversion include: selecting a plurality of unlabeled sentences from a first data source, selecting a plurality of labeled sentences from a second data source, preprocessing a combined corpus of the selected unlabeled and labeled sentences to extract a plurality of linguistic features, generating mixed training data by automatically labeling tokens in the preprocessed corpus based on the plurality of extracted linguistic features, and training a pre-trained model, using the mixed training data, to perform end-to-end C2P conversion.