Voice Assistant with Speaker Identification Project

Voice biometrics has emerged as a secure and convenient alternative to traditional authentication methods. This project presents a Voice-Based User Authentication and Speaker Verification System that utilizes machine learning techniques to identify and verify users based on their vocal characteristics. The system enables users to register and upload multiple voice samples, which are processed using advanced audio feature extraction techniques such as MFCCs, spectral contrast, spectral centroid, zero-crossing rate, and RMS energy. The extracted features are used to train personalized Random Forest classification models capable of distinguishing the genuine user's voice from other voices and background noise. During authentication, a new voice recording is analyzed and compared against the trained model to determine the authenticity of the speaker. The system incorporates data augmentation techniques to improve model robustness and employs Django for web-based user management and audio storage. This solution provides an efficient, scalable, and secure approach to biometric authentication in modern applications.