How to tell if a voice is AI generated

Start here: what kind of clip do you have?

• A full song with vocals and instrumentation — our free AI music detector is built for exactly this. It reads the whole mix, not the voice on its own.
• A dry vocal, a voice note or a spoken clip — this engine is the wrong tool. It measures whole-mix properties that a bare voice recording simply does not have, so it will report low confidence or inconclusive, which is the correct behaviour rather than a useful answer.
• A specific person’s cloned voice — acoustic analysis should be your last step, not your first. Provenance answers this faster than any detector.

We would rather send you away with the right approach than have you paste a two-second clip into a music tool and treat the number as a verdict.

What actually gives synthetic vocals away

These are the cues that tend to matter, in rough order of how often they hold up. None is proof on its own. Several together, in the same recording, are worth taking seriously.

• Breath. Real singers and speakers breathe unevenly, in places dictated by phrasing and lung capacity. Synthetic vocals often either omit breath entirely or insert it as a repeated, identical-sounding token.
• Pitch approach. A human voice slides into notes, overshoots, and settles. Generated and heavily corrected vocals frequently land dead centre with no approach at all. Note that aggressive tuning on a human take produces the same thing.
• Sibilance and plosives. S, T and P sounds carry a lot of transient energy. Models often smear them, or render every instance with suspiciously similar shape and level.
• Room and noise floor. A real recording has a room, a chair, a mic preamp. Synthetic audio tends to have a floor that is either perfectly clean or perfectly constant, with no variation between phrases.
• Emotional continuity. Over a full verse, human delivery drifts: energy builds, a word gets bitten off, a line runs out of air. Generated delivery is often uniformly good and uniformly flat.
• Lyric and word behaviour. Odd stress on the wrong syllable, words that are phonetically plausible but not quite the right word, or pronunciation that shifts between repeats of the same line.

Why software struggles with this

Speech deepfake detection is a real research field with real results, but those results are measured on clean, in-domain audio. Move to a compressed clip pulled off a social feed, a language the model was not trained on, or a background of music and noise, and performance drops sharply. That is why so many “AI voice detector” tools feel confident and are wrong.

The mismatch runs the other way too: pointing a speech-trained detector at a mastered song usually returns confident nonsense, because studio processing looks synthetic to a model trained on bare voice recordings. We wrote that up in detail on why AI audio detection is not AI music detection.

Three different things people mean by “AI vocals”

1. Fully generated vocals — the voice, melody and lyrics all come out of a music generation model. Nothing was sung.
2. Voice cloning — a model trained on a specific person’s voice sings material that person never performed. This is the case with the most serious legal and ethical weight.
3. Voice conversion — a real human sings the take, and a model transforms the timbre onto another voice. The performance, phrasing and breathing are genuinely human.

These leave very different traces. Conversion in particular preserves nearly all of the human performance cues a detector would look for, which is why it is the hardest of the three and the most commonly missed.

Why the mix makes it harder

In a finished master the vocal is compressed, de-essed, pitch-corrected, doubled, saturated, reverberated and sitting under two or three other layers. Every one of those steps removes or masks the fine spectral and temporal detail vocal detection depends on. By the time a track reaches a streaming platform, much of the evidence is gone.

What a credible vocal detector would need

• Source separation to isolate the vocal stem before analysis — and separation itself introduces artefacts that can be mistaken for synthesis
• A vocal-trained model, evaluated separately for generation, cloning and conversion
• Multilingual evaluation, since phonetics and singing style vary enormously
• Held-out voice testing, so performance is not measured on voices the model effectively memorised
• Robustness testing across mix processing and lossy encoding

The AI Music Detector engine does none of this. It applies no source separation, so it cannot make any vocal-specific claim, and it does not pretend to.

What our tool does instead

It measures whole-mix properties. A track with fully generated vocals may still shift those measurements, because generated vocals usually arrive with generated instrumentation — but that is an indirect inference about the track, not a finding about the voice. If you run a song through it, what your result means explains how to read the output without over-reading it.

If you suspect a cloned voice

Acoustic analysis should be your last resort, not your first. Voice cloning cases are usually resolved through provenance: who published it, when, from which account, with what distribution history, and whether the named artist confirms or denies it. Cloning a recognisable artist without permission may also raise personality, publicity and moral rights issues in many jurisdictions, independent of any detector output.

The general verification process is set out in how to check if a song is AI-generated, and the tool’s honest scope is on the accuracy page.

Frequently asked