Language Learning Feedback for Non-Verbal Communication Skills
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Solution Overview
Problem
Conventional shadowing apparatuses primarily focus on improving verbal skills in foreign language learning, neglecting the development of non-verbal communication skills, which are crucial for effective cross-cultural communication.
Innovation Solution
A system that includes a device for capturing a user's non-verbal communication skills during conversation, utilizing machine learning to evaluate and provide feedback on both verbal and non-verbal skills, and offering personalized learning materials to enhance non-verbal communication abilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional shadowing apparatus is used to improve verbal skill, then verbal communication ability is enhanced, but non-verbal communication skills remain undeveloped
Solution Approach 1:
The patent combines verbal skill evaluation (traditional shadowing) with non-verbal skill evaluation (body language, gestures, facial expressions) into a single integrated system. The evaluation apparatus simultaneously captures and assesses both verbal and non-verbal components, creating a comprehensive language learning solution that addresses the gap in non-verbal skill development.
Solution Approach 2:
The system serves multiple functions: it evaluates verbal pronunciation, assesses non-verbal communication skills, provides feedback on both aspects, and generates personalized learning plans. This multi-functional approach ensures that the apparatus can comprehensively address both verbal and non-verbal communication needs in a single device.
2Device complexity
If only verbal skills are evaluated in shadowing apparatus, then evaluation simplicity is maintained, but communication effectiveness is limited
Solution Approach 1:
The evaluation system is segmented into distinct modules: verbal skill evaluation module, non-verbal skill evaluation module, feedback generation module, and learning plan generation module. Each module handles a specific aspect of communication evaluation, making the complex task manageable and systematic while maintaining overall structure and simplicity.
Solution Approach 2:
The patent introduces an intermediary evaluation model that bridges the gap between raw captured data (video, audio) and actionable feedback. This intermediary layer processes and analyzes the data through predefined criteria, translating complex communication behaviors into measurable evaluation results that can be effectively communicated to the user.
3Reliability
If non-verbal communication skills are added to evaluation, then communication effectiveness is improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms that provide real-time or post-session evaluation results to the user. By showing users their performance in both verbal and non-verbal skills, the system creates a closed-loop learning process that motivates improvement and allows users to track their progress, justifying the added complexity through tangible learning benefits.
Solution Approach 2:
The patent defines specific measurable parameters for non-verbal communication skills such as body language accuracy, gesture appropriateness, facial expression alignment, and temporal synchronization. By breaking down complex non-verbal behavior into quantifiable parameters, the system makes evaluation objective and systematic, managing complexity through structured measurement criteria.
Data Source
AI summary
A system for assisting a user to learn foreign languages includes a first device for taking a picture of the user pronouncing in accordance with audio of a moving picture, a first memory storing therein exemplary non-verbal communication skills to be demonstrated by a speaker during conversation, a second memory storing therein a trained evaluation model for evaluating non-verbal communication skills of a speaker during conversation, a second device for comparing the exemplary non-verbal communication skills stored in the first memory to non-verbal communication skills of the user having been acquired by the first device, by means of the trained evaluation model stored in the second memory, to thereby evaluate non-verbal communication skills of the user, and a third device for displaying evaluation made by the second device.