Device Feature based on Graph Fourier Transformation with Logarithmic Processing For Detection of Replay Speech Attacks

TL;DR

This paper introduces new graph Fourier transform-based features incorporating device and environmental noise effects, significantly improving replay speech attack detection in speaker verification systems.

Contribution

The study proposes novel graph frequency domain features, including the graph frequency device cepstral coefficient and logarithmic variants, enhancing replay attack detection accuracy.

Findings

Proposed features outperform existing front-ends on multiple datasets.

Features effectively incorporate device and environmental noise effects.

Demonstrated improved detection performance with GMM and CNN classifiers.

Abstract

The most common spoofing attacks on automatic speaker verification systems are replay speech attacks. Detection of replay speech heavily relies on replay configuration information. Previous studies have shown that graph Fourier transform-derived features can effectively detect replay speech but ignore device and environmental noise effects. In this work, we propose a new feature, the graph frequency device cepstral coefficient, derived from the graph frequency domain using a device-related linear transformation. We also introduce two novel representations: graph frequency logarithmic coefficient and graph frequency logarithmic device coefficient. We evaluate our methods using traditional Gaussian mixture model and light convolutional neural network systems as classifiers. On the ASVspoof 2017 V2, ASVspoof 2019 physical access, and ASVspoof 2021 physical access datasets, our proposed features outperform known front-ends, demonstrating their effectiveness for replay speech detection.