TL;DR
Overall, the main finding is that GRL-based UDA reduces domain shift on ASVspoof 2019 LA relative to the same Custom CNN + CBAM baseline.
The proliferation of sophisticated audio deepfake technology poses a significant threat to digital voice authentication and forensic verification systems. This research addresses this challenge by developing and evaluating a lightweight hybrid Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) architec...
EMBNet is proposed, a task-oriented deepfake speech detection framework that integrates efficient channel attention (ECA) with a multi-scale bottleneck to enhance the representation of subtle acoustic anomalies and may support forensic audio authenticity assessment by improving the discrimination between genuine and ma...
Anti-spoofing systems are important for protecting security-sensitive applications such as financial services, smart assistants, biometric authentication, and access control systems. However, the increasing use of text-to-speech (TTS) and voice conversion (VC) technologies has made it easier to generate spoofed speech...
Recent advances in generative artificial intelligence have enabled highly realistic speech synthesis using text-to-speech (TTS), voice conversion (VC), and neural voice cloning techniques, posing significant security threats to Automatic Speaker Verification (ASV) systems. Conventional handcrafted features such as Mel-...
Rapid advances in text-to-speech (TTS) and voice conversion (VC) have substantially lowered the barrier to generating zero-shot spoofed speech, posing critical challenges for identity authentication, media forensics, and speech security. Current audio deepfake detection methods typically employ large-scale self-supervi...
This study examines the Voxtral audio-language model (ALM) framework for spoofing detection, as a step toward combining CM capabilities within the ALM framework, and proposes an instruction-guided approach that uses label-sequence likelihoods to evaluate bonafide and spoofed speech.
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