Real-time error correction system of spoken English based on multimodal transformer-GCN framework

Key Points

• This research aims to develop a real-time error correction system for spoken English using advanced AI techniques.
• Utilized a multimodal transformer-GCN framework for enhancing language processing.
• Implemented a real-time application for error detection and correction in spoken language.
• Conducted evaluations to measure the effectiveness of the system on language improvement.
• Demonstrated significant enhancements in spoken English accuracy with the new system.
• Achieved a measurable reduction in speech errors during real-time usage.
• Highlighted user satisfaction and engagement with the error correction functionalities.