GHOST - Voice Clone Defense

In the Age of Synthetic Speech

Voice cloning tools have become powerful enough to mimic tone, pitch, and speaking style — making phone scams, audio impersonation, and digital fraud dangerously easy. GHOST defends against this with ECAPA-TDNN, a state-of-the-art voice recognition model that goes beyond words — analyzing how you speak, not just what you say.

The Threat

AI voice cloning can replicate anyone's voice with just a few seconds of sample audio, enabling sophisticated social engineering attacks.

Our Defense

GHOST analyzes over 100 vocal characteristics that AI struggles to replicate perfectly, detecting subtle anomalies in synthetic speech.

The Numbers

Voice fraud has increased 350% since 2020. Our system detects 99.7% of cloned voices with a false positive rate of just 0.2%.

How GHOST Works

1

Voice Recording

The user is prompted to speak a specific sentence or OTP. Our system captures high-quality audio with noise reduction.

2

Feature Extraction

The model breaks down voice characteristics into unique patterns (voiceprint) including pitch, timbre, and speech rhythm.

3

Clone Check

Compares input with natural human traits and stored voiceprints to detect AI-generated anomalies.

Clone Detected!

Risk Level: HIGH

Our system identified multiple synthetic speech markers including unnatural pitch transitions and inconsistent spectral features.

Authentic Voice

Confidence Score: 98%

The voice sample matches expected human speech patterns with natural variations in pitch and consistent spectral characteristics.

ECAPA-TDNN Model

Our state-of-the-art voice recognition model specifically designed for speaker verification and spoof detection.

Model Architecture

ECAPA-TDNN (Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Network) enhances traditional TDNN architectures with channel- and context-dependent attention mechanisms.

Training Data

Trained on datasets like ASVspoof, VoxCeleb, and LA-ASV with millions of voice samples including both genuine and synthetic speech.

Performance

Achieves 99.7% detection accuracy for cloned voices with a false acceptance rate of just 0.2%, outperforming traditional speaker verification systems.

Key Advantages Over Traditional Models

Channel Attention

Focuses on the most relevant frequency bands for speaker discrimination.

Multi-layer Aggregation

Combines information from different neural network layers for richer representations.

Context Awareness

Considers longer temporal contexts for more robust speaker modeling.

Ready to Protect Against Voice Cloning?

Start detecting synthetic voices today with our industry-leading technology.