Enhancing Deepfake Detection: A Study Using WavLM and Advanced RawBoost Augmentation Techniques

Automatic Speaker Verification (ASV) systems are increasingly vulnerable to sophisticated spoofing attacks, particularly those involving deepfake audio. This paper presents our approach to the challenges in Task 1 of the ASVSpoof 2024 competition, focusing on deepfake detection to classify utterances is spoof or bonafide. To enhance model robustness, we employed self-supervised learning (SSL) models, specifically fine-tuning WavLM for feature extraction due to its superior performance in noisy environments. We utilized RawBoost augmentation techniques to simulate real-world audio distortions. Experimental results demonstrate that our approach significantly improves detection accuracy, achieving an EER of 2.85% with WavLM and further reducing to 2.69% with a fusion of WavLM and Wav2Vec2 models.