Client Objective
To develop a mobile app that helps users improve their pronunciation by comparing their spoken words with ideal reference pronunciations.
Solution
We built an AI-driven pronunciation analysis engine integrated into a mobile application that evaluates and scores user pronunciation in real time.
- Used audio fingerprinting techniques to analyze spoken input and compare it with trained voice samples.
- The engine was trained using a dataset of native speaker audio samples to ensure accuracy.
- Implemented machine learning algorithms to detect subtle differences in pronunciation, tone, and stress patterns.
Outcome
- Delivered a user-friendly mobile app that provided instant feedback on pronunciation quality.
- Enabled personalized learning paths by identifying specific sounds and words needing improvement.
- Demonstrated a strong use case for AI in language learning and edtech.
Technologies Used
- Python
- TensorFlow (for audio modeling)
- Audio Fingerprinting
- Mobile app (iOS/Android) integration
- Real-time feedback mechanism
This project showcases our experience in combining speech recognition, machine learning, and mobile technology to solve real-world learning challenges.