IoT and ML-Based Offline Language Learning and Voice Recognition System

This project develops an innovative offline language learning and voice recognition system that integrates voice interaction for educational applications. Utilizing the DFRobot Gravity Voice Recognition Sensor, the system captures voice input and processes it locally without the need for internet connectivity, making it ideal for educational and remote environments.

By leveraging machine learning algorithms, the system can recognize speech patterns, provide feedback, and assist in language learning. The system also integrates with IoT platforms for real-time monitoring and progress tracking, offering users insights into their learning progress. A mobile app allows users to access personalized language learning modules, voice-based interactions, and performance reports.

Key Features:

• Offline voice recognition for language learning applications.
• IoT-enabled real-time data synchronization and progress tracking.
• ML-powered voice recognition and feedback for personalized learning.
• Mobile app for interactive learning, performance insights, and reports.

This project includes source code, complete hardware setup, detailed analysis, and a 200-page project report in soft copy, making it an excellent choice for students exploring IoT and ML in educational technologies.