Voice match checker. Upload two clips and AI estimates whether they're the same speaker.
Select the AI model for audio analysis. Different models may have different capabilities.
Record audio directly from your microphone
To check whether two recordings are the same person speaking, upload audio containing both samples and the AI delivers a verdict (same speaker or different speakers) with a confidence percentage and the evidence behind it: the shared vocal traits it found (pitch range, formants, breathing pattern, cadence, accent) and the differences that suggest different speakers, or, if it judges them the same, the recording-context differences (mic, room, compression) that explain why they sound dissimilar. It also flags anything that could fool a match: impressions, AI voice cloning, and voice changers all get considered rather than ignored. The analysis is upfront about its limits, stating clearly that voice matching without forensic-grade tools is suggestive rather than conclusive. That honesty is the point: it gives you a reasoned, evidence-listed opinion for everyday questions (is this voicemail the same person, is this account using someone else's voice) without pretending to be a courtroom instrument.
Combine your two voice samples into one audio file (one after the other) and upload it. The AI profiles each sample independently across the traits that identify speakers: pitch range and habitual pitch, formant character (the resonance fingerprint of a vocal tract), breathing patterns, cadence and rhythm, and accent and articulation habits. Comparison weighs the overlap: shared traits are listed explicitly, as are mismatches. Context discounting separates speaker differences from recording differences, since a phone call and a studio mic make one person sound like two. Spoof consideration asks whether an impression, voice clone, or voice changer could explain the pattern, which matters more every year. The verdict comes as same or different with a confidence percentage, followed by the caveats section reminding you this is suggestive analysis, not forensic certainty.
Upload one file containing both samples and the checker delivers same or different with a confidence percentage, plus the evidence: shared pitch range, formant character, breathing pattern, cadence, and accent, or the mismatches pointing the other way. It also separates true speaker differences from recording differences like mic and room.
Combine them into a single audio file, one after the other, and upload that. Give each speaker at least 10 to 15 seconds of natural talking. Matching recording conditions helps a lot; heavy compression or echo on one clip can mask exactly the traits the comparison needs.
No. Forensic speaker identification uses controlled samples, specialized measurement, and expert testimony, and even that gets contested in court. This checker gives a reasoned, evidence-listed opinion suitable for everyday questions, not legal ones. If a voice comparison genuinely matters legally, you need a forensic audio examiner, not a web tool.
Treat the verdict as suggestive. Short clips, background noise, illness, deliberate disguise, and emotional state all shift a voice, and confident-sounding percentages can still be wrong. The checker states its confidence and reasoning so you can weigh it; agreement across several different clips means more than any single comparison.
It considers the possibility rather than ignoring it: the spoof section flags patterns consistent with impressions, AI cloning, or voice changers, like unnaturally consistent prosody or formants that do not sit right. Detection is not guaranteed though; good clones specifically exist to pass this kind of listening.
Stable traits that survive context: habitual pitch and its range, the formant fingerprint of a vocal tract, breathing rhythm, cadence, and articulation habits like how someone hits consonants. Content does not matter; two different sentences from one mouth share more acoustic DNA than the same sentence from two mouths.
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