Advanced audio analysis models evaluate speech to identify indications of synthetic manipulation.
Deep learning models that detect synthetic audio
through spectral
signatures
Our system analyzes speech signals for signs of synthetic manipulation by examining spectral and timing patterns.
We convert raw waveforms into structured features, including spectrograms and latent embeddings, then detect deviations from natural speech such as harmonic anomalies, unnatural phoneme transitions, and irregular timing.
By employing both statistical and learned pattern analysis, we can effectively distinguishes genuine human speech from AI-generated audio.