Two research papers submitted to the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2) in 2026 propose novel deep-learning frameworks for detecting manipulated audio. The first paper introduces a dual-branch system using pretrained models XLS-R and BEATs to separately analyze speech and environmental sounds, achieving a 70.20% F1-score. The second paper explores various deep-learning architectures and pretrained models, finding that fine-tuning WavLM with a three-stage strategy yields superior results, with an F1 score of 0.95 on one benchmark dataset. AI
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IMPACT Advances in deepfake audio detection could lead to more robust content moderation and security systems.
RANK_REASON Two arXiv papers present new methods for deepfake audio detection, including specific model architectures and performance metrics.