Device Features Based on Linear Transformation With Parallel Training Data for Replay Speech Detection

Abstract

Replay speech poses a growing threat to speaker verification systems, thus the detection of replay speech becomes increasingly important. A critical factor differentiating replay speech and genuine speech is the representation of device information. Replay speech carries physical device information that originates from recording device, playback device, and environmental noise. In this work, a device-related linear transformation strategy is proposed to disentangle non-device information from replay speech. First, we conduct factor analysis by introducing a common vector for both replay utterance and the corresponding genuine speech utterance on parallel training data; then, we derive an expectation maximization formula to obtain the parameters of the device-related linear transformation; subsequently, three device feature extraction methods are developed based on the device-related linear transformation. The developed device features are evaluated on ASVspoof 2017 version 2.0 and ASVspoof 2021 physical access corpora. The experimental results demonstrate that our proposed linear transformation strategy is effective for replay spoofing detection, and the resultant device features outperform many typical features. Moreover, our spoofing detection systems display superior performance over several competitive state-of-the-art systems.