Lightweight Voice Spoofing Detection Using Improved One-Class Learning and Knowledge Distillation

Voice spoofing detection is a technique for enhancing the security of automatic speaker verification system, but the existing research still faces problems such as weak detection capability and expensive computation. To address these problems, this work presents a lightweight voice anti-spoofing method by using improved one-class learning DOC-Softmax and knowledge distillation. The main idea of DOC-Softmax is to learn a feature space where the genuine samples have a compact space and the spoofing samples are parted from the bona fide space by a certain interval. And the dispersion loss is introduced for spoofing samples to cover the whole spoofing space as much as possible. Moreover, a lightweight voice spoofing detection model is designed to speed up inference, and the knowledge distillation is employed to improve representation power of the lightweight model. Without any data augmentation and ensemble learning, a series of experiments are conducted on LA and PA scenarios of the ASVspoof 2019 dataset, and the experimental results indicate that the proposed method performs better than most existing voice anti-spoofing methods.