ASVspoof-5

The 'ASVspoof 5' dataset is a benchmark used to evaluate the effectiveness of speech deepfake detection systems by providing a collection of spoofed and genuine speech samples.

Papers using ASVspoof-5 (12)

Supervised Post-training of Speech Foundation Models for Robust Adaptation in Speech Deepfake Detection2026Generalizable Speech Deepfake Detection via Information Bottleneck Enhanced Adversarial Alignment2025 · 1 citesCyclostationarity Analysis as a Complement to Self-Supervised Representations for Speech Deepfake Detection2026Quantizer-Aware Hierarchical Neural Codec Modeling for Speech Deepfake Detection2026ASVspoof 5: Evaluation of Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech2026Hybrid Pruning: In-Situ Compression of Self-Supervised Speech Models for Speaker Verification and Anti-Spoofing2025Towards Scalable AASIST: Refining Graph Attention for Speech Deepfake Detection2025Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing2025WavLM model ensemble for audio deepfake detection2024ASASVIcomtech: The Vicomtech-UGR Speech Deepfake Detection and SASV Systems for the ASVspoof5 Challenge2024Spoofing-Robust Speaker Verification Using Parallel Embedding Fusion: BTU Speech Group's Approach for ASVspoof5 Challenge2024Learn from Real: Reality Defender's Submission to ASVspoof5 Challenge2024