Pronunciation Correction System Using Acoustic Deviation Analysis
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Solution Overview
Problem
Current self-driven language learning systems have limited capabilities in automatically detecting pronunciation errors and providing effective feedback for correcting pronunciation, lacking adaptation to the user's native language and learning context.
Innovation Solution
A system that compares user pronunciation with a target pronunciation, generating recommendations based on user-specific and language-specific information, using cognitive analysis and machine learning to provide aural and visual feedback, and continuously learning from user interactions to improve pronunciation correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If self-driven language learning systems are used, then learning flexibility and accessibility are improved, but pronunciation detection accuracy and feedback effectiveness deteriorate
Solution Approach 1:
The system implements automated pronunciation feedback by comparing user pronunciation representations against target pronunciation representations. The feedback mechanism provides tailored corrections based on detected deviations, enabling self-driven learners to receive accurate pronunciation guidance without human instructors. This resolves the contradiction by maintaining learning flexibility while improving pronunciation detection accuracy through computational comparison and analysis.
Solution Approach 2:
The patent replaces manual pronunciation assessment with automated computational systems that use speech recognition and analysis algorithms. The system substitutes human teacher evaluation with machine-based detection of pronunciation deviations, maintaining accessibility while improving measurement precision through sophisticated acoustic analysis and comparison algorithms.
2Productivity
If automated pronunciation detection is implemented, then feedback availability is improved, but adaptability to user-specific context deteriorates
Solution Approach 1:
The system dynamically adapts pronunciation feedback based on user-specific characteristics, native language, and learning progress. The feedback mechanism adjusts its complexity and focus according to the user's individual context, transitioning from generic to personalized corrections. This enables high feedback availability while maintaining adaptability through dynamic adjustment of correction strategies based on user profile and performance data.
Solution Approach 2:
The system changes feedback parameters based on user-specific conditions, including native language background, proficiency level, and individual pronunciation patterns. By adjusting feedback parameters dynamically, the system maintains high productivity in providing feedback while adapting to diverse user contexts and learning needs through parameter modification rather than rigid fixed responses.
3Reliability
If comprehensive user information is collected for tailored feedback, then feedback effectiveness is improved, but system complexity deteriorates
Solution Approach 1:
The system performs preliminary collection and processing of user information during initial setup and ongoing interactions. User profiles, native language data, and pronunciation baselines are established in advance, enabling tailored feedback without requiring complex real-time analysis of all user characteristics. This preliminary action reduces the complexity burden during actual feedback generation while maintaining high feedback effectiveness through personalized corrections.
Data Source
AI summary
Embodiments for assisting pronunciation correction are described. A representation of a user pronunciation of an utterance is received. A representation of a target pronunciation of the utterance is identified. The representation of the user pronunciation of the utterance is compared to the representation of the target pronunciation of the utterance. A recommendation associated with correcting the user pronunciation of the utterance is generated based on the comparing of the representation of the user pronunciation of the utterance to the representation of the target pronunciation of the utterance and information associated with the user.