ABSTRACT:

This project aims to advance speech recognition technology by developing a system to accurately distinguish between original and duplicate voices within a dataset. By employing advanced audio analysis techniques and machine learning algorithms, the system enhances the reliability and accuracy of voice classification. Traditional authentication methods, which often rely on weak passwords, are increasingly vulnerable to security breaches.

To address these vulnerabilities, our voice recognition system ensures that physical presence is required for access, thereby providing a robust security solution. This approach not only improves the precision of voice recognition but also offers significant benefits for individuals with reading and writing challenges, making voice-based interactions more secure and accessible. The integration of these technologies aims to deliver a highly effective and efficient tool for verifying and managing voice-based interactions.

OBJECTIVE:

The primary objective of this project is to develop a sophisticated voice recognition system that accurately distinguishes between original and duplicate voices within a dataset. By employing advanced audio analysis techniques and machine learning algorithms, the project aims to significantly enhance the accuracy and reliability of voice classification.

Additionally, the system seeks to improve security by requiring physical presence for access, thereby addressing the vulnerabilities associated with traditional authentication methods. Another key objective is to facilitate greater accessibility for individuals with reading and writing challenges, offering a practical tool for efficient voice-based interactions. The project also aims to optimize performance to handle large datasets effectively, ensuring fast and precise results. By integrating cutting-edge technologies, the project aspires to deliver a comprehensive and reliable solution for voice recognition.