AI Phonetic Symbol Generation for Reliable GOP Evaluation
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Solution Overview
Problem
Existing language learning systems lack reliability in Goodness of Pronunciation (GOP) evaluation, particularly for polyphonic words and special words, leading to inaccurate pronunciation feedback.
Innovation Solution
A speaking practice system utilizing an AI model with phonetic symbol generation and audio preprocessing systems to generate accurate phonetic symbols for polyphonic words, incorporating audio-symbol and sentence-symbol models to align audio recordings with expected pronunciations, and a GOP evaluation system for reliable scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional phonetic symbol generation methods are used for polyphonic words, then the system structure remains simple, but the pronunciation evaluation reliability deteriorates due to inability to distinguish different pronunciations of polyphonic words
Solution Approach 1:
The patent segments the phonetic symbol generation process by implementing separate audio-symbol models for different polyphonic words. Each model is specialized for specific polyphonic words, allowing the system to accurately generate phonetic symbols for words with multiple pronunciations while maintaining overall system modularity and manageability.
Solution Approach 2:
The system dynamically selects and switches between different audio-symbol models based on the input polyphonic word. This dynamic model selection enables the system to adapt to different pronunciation contexts while keeping the base system structure relatively simple, resolving the contradiction between reliability and complexity.
2Measurement precision
If polyphonic words are not specifically recognized, then the processing speed remains fast, but the pronunciation accuracy deteriorates due to incorrect phonetic symbol generation
Solution Approach 1:
The system performs preliminary recognition of polyphonic words before phonetic symbol generation. By identifying polyphonic words in advance and selecting appropriate audio-symbol models, the system ensures accurate phonetic symbol generation without requiring complex real-time processing, thus maintaining processing speed while improving accuracy.
3Adaptability or versatility
If a single audio-symbol model is used for all words, then the model complexity remains low, but the ability to handle polyphonic words deteriorates leading to evaluation errors
Solution Approach 1:
Each audio-symbol model is designed to be universal for its specific polyphonic word, handling multiple pronunciations of that word across different contexts. This targeted universality approach allows the system to handle polyphonic words effectively while keeping each individual model relatively simple and manageable.
Data Source
AI summary
Goodness of pronunciation (GOP) evaluation techniques with improved reliability are presented. A data preprocessing server operates a data pre-processing system and a GOP evaluation system. The data pre-processing system includes a phonetic symbol generation system and an audio recording preprocessing system. Based on a practice text as well as an audio recording of the user reading the practice text, the phonetic symbol generation system generates phonetic symbols, and the audio recording preprocessing system generates audio data. The GOP evaluation system scores the audio recording based on the phonetic symbols and the audio data. The phonetic symbol generation system operates an artificial intelligence model, which generates the phonetic symbols in response to the fact that the practice text includes polyphonic words. Polyphonic words are words with several pronunciations due to their parts of speech, or special words which are numbers or place names.