Recently, major advances have been made in artificial intelligence and deep learning that allow the generation of very realistic synthetic audio. This circumstance is a big challenge to digital security and public trust. The paper proposes a dependable and quick response system capable of making a difference between a genuine human speech and an AI deepfake audio. It is a hybrid solution that merges feature extraction by OpenAI's Whisper with classification using the Deep Neural Networks (DNNs). The main feature of the system is the ability to detect the key acoustic signatures, e. g. pitch, timbre changes and spectral irregularities, the symptoms of digital "artifacts" that are very difficult to be detected by human hearing. The major goal of this study is to define the optimum search space of the two contradictory objectives of accurate detection and fast operational response, thereby paving the way for the real-time application pipeline enclosing telephonic authentication, financial transactions, and secure communication networks. This is a multi-step approach where the first stage is an audio message capture, followed by ML-based feature extraction, and lastly, classification producing a ready-to-use quality score to alert users about the possible cheating attempt. Experimental outcomes reveal the highlight of the model in dealing with different real-life cases where it offers a scalable way out of the dilemma of recognition in the digital age.

Fake audio detection real time system deepfake audio audio classification machine learning voice spoofing