Speech Intent Recognition for Foreign Language Learners
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Solution Overview
Problem
Current voice recognition devices for foreign language conversation systems have low recognition performance for non-native speakers, limiting their effectiveness and making it difficult for learners to engage in free speech, resulting in unsatisfactory learning outcomes.
Innovation Solution
An apparatus and method that includes a voice recognition device, a speech intent recognition device using skill level and dialogue context-based models, and a feedback processing device to provide customized responses, recommended, right, or alternative expressions, enabling accurate intent determination and adaptive feedback for learners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing voice recognition devices are used for non-native speakers, then the device can recognize speech, but the recognition performance is very low
Solution Approach 1:
The patent applies local quality by creating different speech-based models tailored to specific skill levels (beginner, intermediate, advanced) rather than using a single universal model. Each model is optimized for the characteristics and error patterns of speakers at that particular skill level, thereby improving recognition accuracy for non-native speakers without requiring a completely separate system for each proficiency level.
Solution Approach 2:
The system dynamically changes parameters by selecting different speech-based models based on the user's skill level information. When a user's skill level changes or when the system detects variations in speech quality, it adjusts the model parameters accordingly, allowing the recognition system to adapt to different proficiency levels and maintain high accuracy across diverse user groups.
2Ease of operation
If voice recognition is limited to native speakers, then recognition accuracy is high, but learners cannot freely enter speech
Solution Approach 1:
The patent implements universality by designing a speech recognition system that serves multiple functions: it accurately recognizes native speakers while simultaneously providing tailored support for non-native speakers at various skill levels. The system universally applies intent recognition and feedback mechanisms across all user types, enabling free speech input from learners without sacrificing recognition performance for any group.
Solution Approach 2:
The system uses feedback by providing real-time corrections and suggestions to non-native speakers when recognition confidence is low or errors are detected. This feedback loop allows learners to freely input speech while the system continuously improves recognition accuracy by learning from user corrections and adapting the speech models accordingly.
3Productivity
If the system provides the same response based on scenario, then implementation is simple, but learning effect is not satisfactory
Solution Approach 1:
The patent applies dynamics by making the system response adaptive rather than static. The feedback processing device dynamically generates responses based on the user's skill level, dialogue context, and speech intent, rather than providing fixed scenario-based responses. This dynamic adaptation significantly improves learning effectiveness by tailoring feedback to each learner's specific needs and progress stage.
Solution Approach 2:
The system segments the feedback mechanism into multiple components: speech-based models for different skill levels, dialogue context-based models for situational understanding, and intent recognition modules for determining user goals. This segmentation allows the complex system to be managed through modular components, each handling a specific aspect of the interaction, thereby improving learning effectiveness without overwhelming system complexity.
4Measurement precision
If skill level-specific models are used, then speech intent recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The patent manages model complexity through parameter changes by dynamically selecting and switching between different speech-based models based on the user's skill level. Rather than maintaining all models simultaneously active, the system changes the active model parameters according to user profile and context, achieving high intent recognition accuracy while keeping the operational complexity manageable through parameter-driven model selection.
Data Source
AI summary
The apparatus for foreign language study includes: a voice recognition device configured to recognize a speech entered by a user and convert the speech into a speech text; a speech intent recognition device configured to extract a user speech intent for the speech text using skill level information of the user and dialog context information; and a feedback processing device configured to extract a different expression depending on the user speech intent and a speech situation of the user. According to the present invention, the intent of a learner's speech may be determined even though the learner's skill is low, and customized expressions for various situations may be provided to the learner.