AI Avatar VESL Program Using Speech Recognition and Neural Networks
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Solution Overview
Problem
Converting vocational English as a Second Language (VESL) programs to a full online course is challenging due to the difficulty in providing in-person conversation courses and practice with various pronunciations, which are essential for evaluating students' speaking and pronunciation skills.
Innovation Solution
The use of Artificial Intelligence (AI) and avatar technologies to create an AI-based online VESL program that utilizes speech recognition, attention neural networks, and speech synthesis to provide a low-pressure learning environment for non-native speakers, allowing them to practice conversations and pronunciation through AI avatars.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-person conversation courses are used to evaluate students' speaking and pronunciation skills, then measurement precision is improved, but device complexity and ease of operation worsen due to the need for physical presence and human instructors
Solution Approach 1:
The patent creates virtual copies of human instructors through AI-powered avatar characters that can evaluate and provide feedback on students' speaking and pronunciation skills. These digital avatars replicate the instructional functions of human teachers, eliminating the need for physical presence while maintaining evaluation capabilities through speech recognition and analysis technologies.
Solution Approach 2:
The patent replaces the mechanical system of in-person physical interaction with automated digital systems. Speech recognition software, natural language processing, and automated evaluation algorithms substitute for human instructors' physical presence and manual assessment, enabling remote pronunciation evaluation through computer-based technologies.
2Manufacturing precision
If in-person conversation practice is provided for various pronunciations, then manufacturing precision of language skills is improved, but ease of operation deteriorates due to logistical complexity
Solution Approach 1:
The patent creates universal AI tutor avatars that can serve multiple functions: teaching various pronunciation styles, providing conversation practice, evaluating student performance, and adapting to different learning levels. These multi-functional virtual instructors can handle diverse language teaching needs through a single platform, making language practice accessible without the logistical complexity of arranging multiple specialized in-person sessions.
Solution Approach 2:
The patent enables students to engage in self-directed language practice through automated AI tutors that provide immediate feedback and guidance without requiring human instructor availability. The system allows students to practice conversations and pronunciation independently, with the AI avatar serving as an always-available practice partner that adapts to student needs without scheduling constraints.
3Reliability
If human instructors conduct conversation evaluation, then reliability of assessment is improved, but productivity decreases due to limited instructor availability
Solution Approach 1:
The patent creates multiple instances of instructor functionality through AI avatar copies that can simultaneously evaluate numerous students. Each virtual tutor replicates the assessment capabilities of a human instructor, allowing one system to serve many students concurrently without sacrificing evaluation quality, thereby dramatically increasing the effective student-to-instructor ratio.
Solution Approach 2:
The patent transforms the assessment process by changing key parameters: replacing human cognitive evaluation with automated speech analysis algorithms, converting subjective human judgment into objective measurable metrics through speech recognition technology, and enabling parallel processing of multiple student assessments simultaneously through computational systems.
Data Source
AI summary
In embodiments, a computer implemented method for language learning includes delivering a user or student's speech into a computer using speech recognition software and sending the output of the speech recognition software to a language adaptive encoding module which outputs a positional encoding matrix; inputting the positional encoding matrix to a trained attention neural network module comprising an encoder and decoder block and a feed forward neural network layer, wherein the trained attention neural network module is trained using course materials for language learning, receiving the output of the trained attention neural network module into a speech synthesis module, delivering the output of the speech synthesis module to an avatar on the computer; and delivering speech from the avatar to the student.