🎙️ Voice Biometric Authentication System

Overview

This project implements a voice biometric authentication system that enables secure, passwordless user authentication using voiceprints and spoken passphrases. The system combines biometric identity verification (“who you are”) with spoken content validation (“what you know”), forming a multi-factor authentication workflow.

The system is designed as a cloud-native ML application, emphasizing low-latency inference, secure handling of biometric data, and production-ready deployment patterns.

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Problem Statement

Password-based authentication systems are vulnerable to reuse, leakage, and phishing. Voice biometrics offers a frictionless alternative but introduces challenges around:

• Speaker variability
• Spoofing and replay attacks
• Latency at inference time
• Secure storage and retrieval of biometric embeddings

This project addresses these challenges through deep learning–based voice embeddings, vector similarity search, and spoof detection, deployed in a scalable cloud environment.

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Planned System Architecture

1. Audio Capture

   • Users record voice samples during registration and authentication.
   • Inputs include name, passphrase, and free-form speech.

2. Feature Extraction & Embedding

   • Acoustic features: MFCCs, Mel Spectrograms
   • Deep voice embeddings: Wav2Vec2
   • Speech transcription: Whisper

3. Vector Storage & Retrieval

   • Voice embeddings stored in a vector database (Milvus / Zilliz Cloud)
   • Cosine similarity used for identity matching

4. Authentication Logic

   • Voice similarity scoring
   • Passphrase transcription validation
   • Spoof and replay attack detection prior to access approval

5. Deployment

   • Containerized application using Docker
   • Serverless deployment on GCP Cloud Run

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Technology Stack (Target)

ML & Signal Processing

• MFCCs, Mel Spectrograms
• Wav2Vec2 embeddings
• Whisper (speech-to-text)

Backend

• Python
• Flask

Vector Database

• Milvus (Zilliz Cloud)

Cloud & Infrastructure

• Docker
• Google Cloud Platform (Cloud Run)

Security

• CSRF protection
• Audio file validation and secure naming
• Environment-based secret management

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Monitoring & Production Considerations

This system is designed with production ML principles in mind:

• Deterministic embedding generation
• Secure handling of biometric data
• Configurable similarity thresholds
• Deployment patterns compatible with monitoring and model versioning

Planned extensions include:

• Embedding drift detection
• Authentication score monitoring
• Model version comparison for regression analysis

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System Walkthrough

A recorded walkthrough of the system design, architecture decisions, and deployment considerations is available here:

▶️ YouTube: https://youtu.be/dADQn3ONOU8?si=7SYuS-Ok7g9biq_R

The walkthrough covers:

• End-to-end authentication flow
• Feature extraction and embedding strategy
• Vector database usage for similarity search
• Security and deployment considerations