Defense Against Synthetic Speech: Real-Time Detection of RVC Voice Conversion Attacks

• URL: http://arxiv.org/abs/2601.04227v1
• Date: Wed, 31 Dec 2025 02:06:42 GMT
• Title: Defense Against Synthetic Speech: Real-Time Detection of RVC Voice Conversion Attacks
• Authors: Prajwal Chinchmalatpure, Suyash Chinchmalatpure, Siddharth Chavan,
• Abstract summary: Generative audio technologies now enable highly realistic voice cloning and real-time voice conversion.<n>This study investigates real-time detection of AI-generated speech produced using Retrieval-based Voice Conversion.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative audio technologies now enable highly realistic voice cloning and real-time voice conversion, increasing the risk of impersonation, fraud, and misinformation in communication channels such as phone and video calls. This study investigates real-time detection of AI-generated speech produced using Retrieval-based Voice Conversion (RVC), evaluated on the DEEP-VOICE dataset, which includes authentic and voice-converted speech samples from multiple well-known speakers. To simulate realistic conditions, deepfake generation is applied to isolated vocal components, followed by the reintroduction of background ambiance to suppress trivial artifacts and emphasize conversion-specific cues. We frame detection as a streaming classification task by dividing audio into one-second segments, extracting time-frequency and cepstral features, and training supervised machine learning models to classify each segment as real or voice-converted. The proposed system enables low-latency inference, supporting both segment-level decisions and call-level aggregation. Experimental results show that short-window acoustic features can reliably capture discriminative patterns associated with RVC speech, even in noisy backgrounds. These findings demonstrate the feasibility of practical, real-time deepfake speech detection and underscore the importance of evaluating under realistic audio mixing conditions for robust deployment.

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