Most detectors look for AI artifacts. We also look for what makes a voice biologically human — and that distinction is what makes detection resilient.
Searches for artifacts left behind by synthetic generation. As voice cloning improves, those artifacts disappear — and the detector degrades with them. A purely artifact-based approach puts defenders in a perpetual catch-up position.
Also analyzes biological signals present in genuine speech — micro-variations in breath, tension, and vocal fold behavior that synthetic systems struggle to replicate consistently in real time.
Synthetic voice technology evolves rapidly. An attacker-minded testing approach helps improve resilience as the threat landscape changes. PulmoForge is continuously evaluated against a range of synthetic speech systems.
Codec-compressed audio — specifically Zoom's Opus 32kbps pipeline — is a core training condition, not an afterthought.
| Test category | Coverage |
|---|---|
| Commercial voice cloning systems | Continuously tested |
| Open-source speech generators | Continuously tested |
| Emerging synthetic speech models | Monitored and evaluated |
| Codec-compressed audio (Opus 32kbps) | Core training condition |
AI-generated voices are becoming increasingly difficult to identify by ear. The gap between synthetic and natural speech is narrowing at a pace that outpaces human perception.
Organizations need tools that can provide additional confidence when important decisions depend on the identity of the speaker. PulmoForge is building technology designed to help verify that trust — in real time, during the call, before any damage is done.
Free beta access goes to the waitlist first — no spam, just a single email when it's ready.