How Does Voice Clone Detection Work?
AI voice cloning systems generate synthetic speech by learning to reproduce the spectral and prosodic characteristics of a target speaker. While the output sounds convincing to human ears, the synthesis process leaves characteristic artifacts at the frequency level. AIGeneratedIt's detection models analyze audio at multiple layers: raw waveform artifacts captured by RawNet2, learned feature representations from Wav2Vec2, and hand-crafted spectral fingerprints via MFCC-CNN analysis.
Cloned voices produced by systems like ElevenLabs, VALL-E, and Resemble.ai typically show anomalies in Mel-frequency cepstral coefficients (MFCCs), irregular breath patterns, unnatural formant transitions between phonemes, and subtle compression artifacts in high-frequency bands above 8kHz. Our CNN-BiLSTM model captures temporal dependencies across the audio sequence to detect inconsistencies in speaking rhythm and intonation contours that are characteristic of concatenative or neural TTS synthesis.
All three models in the ensemble vote independently, and their confidence scores are combined using a learned fusion layer that weights each model based on the type of audio content detected. This produces a single AI probability score with an explanatory breakdown of which artifacts were found.
Frequently Asked Questions
How accurate is the audio deepfake detector?
Our audio detection ensemble achieves 96% accuracy on the ASVspoof 2021 LA and DF tracks — the industry-standard benchmark for anti-spoofing research. In internal testing against real-world voice clones from ElevenLabs and Resemble.ai, we achieve 94% accuracy with a false-positive rate under 3%.
Which voice cloning tools can it detect?
AIGeneratedIt is trained on synthetic audio from ElevenLabs, VALL-E, VALL-E X, Resemble.ai, Tortoise TTS, Bark (Suno), Coqui TTS, Microsoft Azure Neural TTS, Amazon Polly, Google WaveNet, RVC (Retrieval-based Voice Conversion), and 15+ additional tools. The training dataset is updated quarterly to include newly released systems.
Can it detect phone call recordings or compressed audio?
Yes. AIGeneratedIt is robust to common audio compression including MP3 at 128kbps, phone call quality audio at 8kHz sample rates, and social media-compressed audio. However, heavily compressed audio (below 64kbps) may reduce accuracy. For best results, use uncompressed WAV or high-quality MP3.
What is the maximum audio file size?
Free users can upload files up to 50MB. Pro users can upload up to 500MB. Audio files under 10 minutes in length return synchronous results in under 8 seconds. Longer recordings are queued for asynchronous processing and results are delivered via email notification.
Is this tool suitable for legal or forensic use?
AIGeneratedIt is used by journalists, legal professionals, and law enforcement agencies as a preliminary screening tool. Each scan generates a forensic report with confidence scores, model attribution, and spectral evidence. For court-admissible evidence, we recommend pairing our report with a certified digital forensics expert.
Voice Cloning Tools This Detector Covers
Our training dataset includes audio from all major voice cloning and text-to-speech synthesis platforms currently in use:
- ElevenLabs — Multilingual v2, voice cloning, voice design
- Microsoft VALL-E & VALL-E X — zero-shot TTS voice replication
- Resemble.ai — custom voice cloning with emotion control
- Tortoise TTS — high-fidelity open-source voice cloning
- Bark (Suno AI) — generative audio with non-speech sounds
- Coqui TTS — open-source neural TTS
- RVC (Retrieval-based Voice Conversion) — community voice models
- Microsoft Azure Neural TTS — enterprise speech synthesis
- Google WaveNet & SoundStorm — waveform generation models
- Amazon Polly — cloud-based speech synthesis