Classical Machine Learning Baselines for Deepfake Audio Detection on the Fake-or-Real Dataset

The project serves as a scientific baseline rather than a commercial product or a novel software tool. It applies standard, well-documented classical machine learning techniques (Random Forest, SVM) to the 'Fake-or-Real' (FoR) dataset. While interpretability is a valid academic pursuit, the defensive moat is non-existent as the techniques used (feature extraction of MFCCs, jitter, and shimmer) have been standard in speech processing for decades. From a competitive standpoint, frontier labs like OpenAI (Voice Engine) and specialist firms like ElevenLabs or Pindrop utilize massive transformer-based architectures that far outperform classical ML in generalization across diverse acoustic environments. The project has zero stars and minimal activity, typical for a recent academic upload. Its primary value is as a benchmark for researchers, not as a standalone solution. Large cloud providers (AWS, Google Cloud) already offer or will soon offer superior 'black-box' detection APIs that render manual feature-engineering-based approaches obsolete for most production use cases.