Voice biometric authentication PAM module for Linux

Voice authentication system implementation using Python

A voice authentication system leveraging Deep4SNet for DeepFake detection and GMM UBM for speaker verification. It provides a robust solution for securing voice-based authentication applications.

A secure GUI-based voice authentication system for Windows startup, built in Python with MFCC + DTW.

This project implements an AI-powered Voice Word Lock system that authenticates users using a fixed spoken passphrase combined with speaker verification and voice biometrics. The system captures voice input, extracts biometric features, and verifies the speaker’s identity using machine learning models. Access is granted only when both the spoken

LOCKERIT is an offline, cross‑platform secure vault: dynamic key rotation, dual-factor (password + voice biometrics), device binding, phrase-based recovery, OTP‑guarded re-enrollment, and container encryption (v2 dual KEK wrap) using AES‑256‑GCM and XChaCha20‑Poly1305 with Argon2id.

Speaker recognition using Gaussian Mixture Models (GMM) involves identifying speakers by modeling their voice features with statistical distributions. GMMs are trained on speech data to distinguish between different speakers based on their unique vocal characteristics. This technique is commonly used in voice biometrics and authentication systems.