Pronunciation Assessment Decision Tree for Language Learning Feedback
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Solution Overview
Problem
Existing language learning systems fail to provide adequate feedback and correction information for pronunciation, limiting their effectiveness and user satisfaction.
Innovation Solution
A language learning system and method that uses an assessment decision tree with decision paths and nodes to diagnose pronunciation features and provide detailed feedback, incorporating feature extraction and decision tree generation to analyze and correct pronunciation errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pronunciation assessment techniques (Gaussian model, Gaussian mixture model, pronunciation verification) are used, then the system can provide overall weighted assessment or single mark, but the language learner cannot obtain adequate feedback information and correction information
Solution Approach 1:
The patent segments the pronunciation assessment into multiple dimensions by creating a decision tree with different decision paths. Each path corresponds to specific pronunciation features (such as vowel sounds, consonant sounds, tone, stress, intonation) and provides targeted feedback for each segment. This segmentation transforms the single-mark assessment into multi-dimensional diagnostic feedback, resolving the contradiction between measurement precision and information completeness.
Solution Approach 2:
The patent adds a new dimension to pronunciation assessment by introducing a decision tree structure with multiple decision paths. Instead of providing a single scalar mark, the system generates a tree of diagnostic paths where each path represents a different aspect of pronunciation quality. This dimensional expansion allows the system to maintain precise measurement while providing comprehensive feedback information across multiple assessment dimensions.
2Reliability
If the language learning system provides detailed diagnosis and feedback information for each pronunciation, then the learning effectiveness is improved, but the system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-building the decision tree structure with all possible decision paths and feedback information before the actual pronunciation assessment. The decision tree is constructed in advance based on language learning theories and pronunciation assessment criteria. During runtime, the system only needs to traverse the pre-built tree based on extracted pronunciation features, which simplifies the real-time processing complexity while maintaining comprehensive diagnostic capability and high learning effectiveness.
3Measurement precision
If the system compares learner pronunciation with professional audio files and analyzes differences, then pronunciation assessment is provided, but adequate feedback and correction information is not given
Solution Approach 1:
The patent implements comprehensive feedback by integrating the decision tree assessment with the traditional audio comparison method. The system not only compares learner pronunciation with professional references to achieve accurate measurement but also uses the decision tree to generate detailed feedback information along each decision path. Each path provides specific correction guidance based on the identified pronunciation issues, thus resolving the contradiction between measurement precision and correction information completeness.
Data Source
AI summary
A language learning system including a storage module, a feature extraction module, and an assessment and diagnosis module is provided. The storage module stores training data and an assessment decision tree generated according to the training data. The feature extraction module extracts pronunciation features of a pronunciation given by a language learner. The assessment and diagnosis module identifies a diagnosis path corresponding to the pronunciation of the language learner in the assessment decision tree and outputs feedback information corresponding to the diagnosis path. Thereby, the language learning system can assess and provide feedback information regarding words, phrases or sentences pronounced by the language learner.