Catch a cloned voice while the call is still live
Detect cloned and synthetic voices on conference and VoIP calls as the conversation happens, whether the voice belongs to an executive, a vendor, or a candidate.
From signals to one action your team can take.
- Cloning detectedAudioSynthesis artifacts consistent with a voice-cloning or text-to-speech model
- Full call scoredCoverageEnough clean speech to evaluate end to end, not a partial pass
- Voice onlyChannelNo video on this call, so nothing corroborates the audio
- Urgency risingConversationPressure and an escalating ask alongside the audio flag
Hold the line. A synthetic-voice verdict reaches your team while the call is live, with a recommended next step.
How voice scoring runs
The same pipeline runs on a live call and on an audio file uploaded to the detector. It analyses the audio signal, not the speaker.
- 1
Segment the audio
Speech is split into short consecutive segments so the call can be scored as it happens rather than judged once at the end.
Continuous · live or uploaded file
- 2
Score each segment
Every segment is scored independently for the artifacts left by voice-cloning and text-to-speech models. No reference recording of the real person is needed.
No enrollment · no voice profile stored
- 3
Keep scoring to the end
Scoring runs for the length of the call, not just an opening sample, so a handoff to a different speaker or a voice that only appears at the ask is still scored.
Real time · full call duration
- 4
Resolve with coverage
Segments combine into one band, and how much usable speech was actually evaluated is reported alongside it rather than assumed.
Bands: AI · mixed · clean · inconclusive
Where voice cloning shows up
- 01
Cloned executives by phone
A few seconds of public audio is enough to clone a voice convincing enough to move a wire or force an exception.
- 02
Spoofed vendor and candidate calls
A cloned vendor confirming new banking details, or an altered candidate voice on a screening call.
- 03
Callbacks that confirm nothing
The callback is supposed to be the control that catches a fraudulent change. It only works if the voice that answers is real, and a clone answers just as convincingly as the person it copies.
How a voice-cloning attack unfolds
These attacks move through a recognizable sequence. Diopter scores that sequence while the call is still in progress.
A familiar voice calls
An executive, a vendor contact, or a candidate, recognizable enough that the request feels routine.
Urgency arrives early
A closing window, an overdue invoice, or a competing offer compresses the time to verify.
The call moves off-channel
The conversation shifts to a private line or a follow-up that keeps others out of it.
The asks escalate
A small confirmation becomes a larger request as the call builds on each prior yes.
The action is taken
A wire, a banking change, or an offer is acted on while the voice is still trusted.
The calls where there is no camera to check.
Voice-only channels are where this capability matters most, because every other verification signal a person would use is absent.
Inbound phone calls
Voice-only calls with no video to fall back on, which is where cloning is cheapest for an attacker and hardest for a person to catch.
Treasury authorization
The verbal approval on a transfer, scored while it is being given rather than reconstructed afterwards.
Supplier confirmations
The callback that is supposed to confirm a banking change, which only helps if the voice answering it is real.
Remote screening calls
Early-round candidate calls where a cloned or altered voice is doing the talking.
This is not voice recognition, and that is the point.
Almost every tool in this category works by matching a voice against an enrolled sample of the real person, which means it needs a recording of everyone it protects, a database of voiceprints to secure, and an enrollment process nobody completes. Diopter does none of that. It scores the audio itself for the artifacts that cloning and text-to-speech models leave behind, so it works on a caller you have never heard before, on the first call, with nothing stored about anyone's voice.
A cloned voice passes a single listen. The script it runs, urgency, isolation, and the ask, gives it away across the call.
Where single-layer tools stop.
Each category below covers one part of the attack and is blind to the rest. The last column is the only one that correlates them into a single verdict.
Detects synthetic voice on a live call
- Awareness training
- Single-frame deepfake
- Identity / reputation
- Live-call detection
- Diopter Arc
Detects deepfake video frames
- Awareness training
- Single-frame deepfake
- Identity / reputation
- Live-call detection
- Diopter Arc
Verifies caller identity (reputation/biometric)
- Awareness training
- Single-frame deepfake
- Identity / reputation
- Live-call detection
- Diopter Arc
Models the conversation arc (pressure → ask)
- Awareness training
- Single-frame deepfake
- Identity / reputation
- Live-call detection
- Diopter Arc
Correlates identity, media, and conversation signals on live calls
- Awareness training
- Single-frame deepfake
- Identity / reputation
- Live-call detection
- Diopter Arc
Forensic evidence chain for incident review
- Awareness training
- Single-frame deepfake
- Identity / reputation
- Live-call detection
- Diopter Arc
| Capability | Awareness training | Single-frame deepfake | Identity / reputation | Live-call detection | Diopter Arc |
|---|---|---|---|---|---|
| Detects synthetic voice on a live call | |||||
| Detects deepfake video frames | |||||
| Verifies caller identity (reputation/biometric) | |||||
| Models the conversation arc (pressure → ask) | |||||
| Correlates identity, media, and conversation signals on live calls | |||||
| Forensic evidence chain for incident review |
What voice detection does not claim
The second item is the one most often assumed about voice tools, and it is worth being explicit that we do not do it.
It is not speaker identification
Diopter answers whether the audio is synthetic, not who is speaking. It does not build voiceprints, does not store voice profiles, and needs no prior recording of the real person to work.
It does not judge accent or origin
The score comes from generation artifacts in the signal. How someone sounds, where they are from, and what language they speak are not inputs, and must never be treated as risk signals.
Short or degraded audio is inconclusive
A few seconds of speech, heavy background noise, or a badly compressed line may not carry enough signal to judge. That is reported as inconclusive rather than passed off as clean.
Light to deploy, clear about what runs where.
Pilot in days, roll wider through MDM, and keep sensitive call media inside your perimeter.
- On-prem and hybrid deployments supported
- No caller-side install
- Bot or bot-free capture
- Configurable retention, including ZDR
- MDM rollout (Intune, Jamf)
- SOC 2 Type II in progress
Walk an attack arc with Diopter.
We will replay a real incident, show the signals Diopter scored, and map the verdict your team would act on. We will sign your NDA first if you want one.