Voice Deepfake Detection: How It Works

As synthetic voice technology advances, distinguishing genuine human speech from AI-generated audio has become a critical challenge. ORAVYS addresses this with thousands of independent forensic analyses that examine voice at the level of individual audio frames.

The Growing Threat of Voice Deepfakes

Modern text-to-speech and voice cloning systems can generate remarkably convincing synthetic audio. From financial fraud conducted via cloned executive voices to fabricated evidence in legal proceedings, the stakes are enormous. Traditional audio forensics tools rely on a handful of acoustic features and struggle to keep pace with rapidly evolving generative models.

ORAVYS takes a fundamentally different approach. Rather than looking for artifacts left by a specific synthesis method, the platform analyzes acoustic signals that are inherently present in genuine human speech and absent or distorted in synthetic audio.

Proprietary Ensemble Architecture

ORAVYS combines deep-learning models with thousands of independent specialized analyses. The system captures both short-term acoustic patterns and long-range speech dynamics within a single utterance.

Each analysis operates on a rich set of acoustic measurements across several dimensions. The architecture details, model weights, and fusion logic are proprietary and protected.

Thousands of Specialized Analyses

Beyond the central classification model, ORAVYS runs thousands of specialized analyses in parallel. Each analysis operates independently and contributes its own confidence score, which is then fused into a single verdict.

This multi-analysis design provides resilience against adversarial attacks. Even if a sophisticated deepfake fools a handful of individual analyses, the collective assessment across dozens of independent signal pathways produces a reliable, defensible result.

EU AI Act Article 50

ORAVYS supports Article 50 documentation. This is our own assessment; it has not been reviewed by a regulator or outside counsel. The platform never makes deterministic claims about any individual. Instead, it reports voice authenticity scores and acoustic anomalies, leaving the final judgment to qualified human professionals. All outputs include confidence intervals and methodology transparency.

Honest About Its Limits

Every paid result falls in one of three bands: strong synthetic signals, no strong synthetic signal, or inconclusive. Results are indicative, not definitive, and a recording that does not support a firm call is reported inconclusive rather than forced into a verdict.