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Detect iSpeech AI voices.

Updated July 2026

Suspect a clip was made with iSpeech? Drop it in and get a citable verdict with the model named, in under half a second. Long-standing cloud text-to-speech and voice API used in apps and phone systems.

Synthetic detectediSpeech
Confidence
high
Model
iSpeech
Yes, you can check. Drop the clip into the detector and it returns the probability that the speech was generated by iSpeech, a confidence level, and the named model, in under half a second. It reads the audio's synthesis signature, so it works on cloned and stock iSpeech voices alike, and gives you a citation URL to quote.
What it is

iSpeech, and why it shows up in suspicious audio

iSpeech is a long-established text-to-speech and voice API used in apps, accessibility tools, and phone systems (IVR). Its voices turn up in automated calls and notifications.

Because it is often delivered over telephony, iSpeech clips arrive compressed, the harder case a detector must handle honestly.

Where you tend to see it: IVR and phone systems, app narration, and automated notifications.

iSpeech is a legitimate product; misuse is the problem, not the tool. You can read about it on the official iSpeech site.

The tells

How to tell a iSpeech voice

The human ear is unreliable on current iSpeech audio. These are the signals a detector weighs. We report which ones drove the verdict rather than handing you a bare number.

  • 01Production-TTS pacing that does not react to meaning.
  • 02Artifacts that persist through telephony-grade compression.
  • 03Uniform spectral balance across a clip.
  • 04Even, unvarying energy across long passages.
Spectral view · artifacts concentrate where synthesis smooths what a human voice would not
How the detector identifies iSpeech

A verdict you can cite, not a vibe

The detector reads the audio, not the speaker. Cloning or stock voice, clean or compressed, it looks for the synthesis signature and attributes the source.

Step 1

Drop the clip

Upload a file or paste a URL. MP3, WAV, M4A, WebM, or the audio track of a video. About half a second of clear speech is enough.

Step 2

The model scores it

The same model behind the public detector reads the acoustic signature and weighs the artifacts, then attributes the source, e.g. iSpeech, when it recognizes it.

Step 3

Get a citable verdict

You get a probability, a confidence level, the named model, and a permanent citation URL you can quote, file, or subpoena.

0.48s
Median verdict
99%
Accuracy on clean audio
24+
Generators covered
24h
Audio deleted after
If a clip turns out synthetic

What to do next

A verdict is evidence, not a verdict of intent. Save the result to get a permanent citation URL and a one-way audio fingerprint you can reference later without storing the file. If you are a journalist or investigator, cite the verdict alongside your own reporting; if this is a suspected scam, treat the contact as unverified and confirm through a channel you already trust. The FTC's advice on suspected scam calls is a sensible baseline.

Building this into a workflow? The API returns the same verdict as JSON with webhooks for bulk jobs, and the browser extension checks audio in place on WhatsApp Web, YouTube, and podcasts.

FAQ · iSpeech

Common questions

Can you detect iSpeech on a phone-quality clip?
Often, yes. Compression lowers confidence but rarely erases the signal, and we report the confidence so a low-quality clip is not mistaken for certainty.
Does the source app matter?
No. The detector reads the audio itself, wherever the iSpeech voice was produced.
Is there an API for bulk checks?
Yes. The developer API returns the same JSON verdict with webhooks for bulk jobs.
Free to try?
Yes, a single verdict is free with no card.
Will it name iSpeech specifically?
When recognizable, yes; otherwise it returns 'unknown synthesis'.

Is this a iSpeech voice? Find out.

Free verdict, model named, in under a second. No card to start.

Open detectorUse the API