Audio spoofing detection

Abstract

Efficient and accurate audio spoofing detection is crucial to ensuring audio-based systems’ security and integrity. Existing methods often mainly focused on the performance of the detection system. This master thesis focuses on the development of advanced techniques that prioritize efficiency while maintaining high detection performance. We introduced the model, consisting of an encoder and a classifier, which can efficiently learn complex representations with a lack of labeled data. We introduce suitable loss functions to effectively distinguish spoofed and bonafide speech in latent space to keep the performance high. The results demonstrate notable improvements in both encoder performance and classification accuracy, highlighting the potential for enhanced self-supervised audio analysis techniques. Keywords: self-supervised learning, audio spoofing detection, automatic speaker verification, audio processing, speech classification