Upload any voice recording and instantly detect whether it is a real human voice or artificially generated speech. Our advanced detection engine analyzes acoustic fingerprints, frequency signatures and neural speech artifacts to deliver accurate authenticity results within seconds.
Select your audio file below and our AI engine will analyze speech characteristics to determine whether the voice is natural human speech or AI generated synthetic audio.
Trained on thousands of real and synthetic voices for high precision detection results.
Detects authenticity even for mobile and laptop microphone recordings.
Get real-time detection results within seconds after uploading audio.
Audio files are processed temporarily and never permanently stored.
Our detection engine analyzes deep acoustic fingerprints inside speech signals using neural learning models trained on thousands of real human recordings and synthetic AI voices.
Upload microphone recordings, voice notes, podcasts, interviews or AI-generated audio. The system supports WAV and MP3 formats for fast detection.
Advanced acoustic analysis extracts MFCC speech signatures, tone structure and waveform transitions used to differentiate natural voices from AI synthesis.
Deep learning compares your recording with thousands of real and synthetic voice datasets to identify hidden machine-generated speech characteristics.
The detector calculates probability confidence and instantly shows whether the audio is real human speech or AI-generated synthetic voice.
Even the most advanced AI voice generators leave detectable acoustic fingerprints that differ from natural human speech production. Our system analyzes harmonic structure irregularities, waveform transitions, breathing variations and timing precision artifacts that synthetic voices cannot perfectly replicate. This allows the detector to accurately classify deepfake speech, cloned voices and neural text-to-speech recordings across real-world environments.
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Our voice detection engine uses deep neural speech analysis trained on thousands of real and synthetic voice recordings. Accuracy typically ranges between 90% and 97% depending on recording clarity, microphone quality and background noise conditions.
Yes. The system is trained to recognize artifacts produced by modern voice cloning engines such as neural TTS models. It analyzes timing uniformity, spectral inconsistencies and harmonic structure differences to identify cloned speech patterns.
Absolutely. The model has been trained using microphone captured datasets including mobile voice notes and compressed audio formats. This allows reliable predictions even when recordings are not studio quality.
No. Uploaded audio files are processed temporarily during prediction and automatically removed afterward. The system does not permanently store recordings unless optional analytics logging is enabled by administrators.
Modern AI speech can sound natural to human ears, but machine learning detectors evaluate deeper acoustic fingerprints that are invisible to listeners. These include spectral smoothing behavior, phase alignment patterns and waveform transition uniformity typical in synthetic audio.