A Green Learning Approach to Spoofed Speech Detection

A green-learning-based spoofed speech detector that decides whether an input speech sample is bona fide (genuine) or spoofed in an automatic speaker verification (ASV) system, is proposed in this work. The proposed solution, called the green ASVspoof detector (GAD), adopts Wav2vec (version 2.0) speech representations as its front-end model. We partition an input speech sample into temporal segments and adopt the Wav2vec representation for each segment. Then, GAD is a 3-stage decision process comprising one XGBoost classifier in each stage. It offers an interpretable design. It is shown by experimental results that GAD achieves competitive performance in ASVspoof detection. At the same time, it has a smaller model size and significantly lower computational complexity, thus positioning it as an effective and green solution.