Transmission Line Cochlear Model Based AM-FM Features for Replay Attack Detection

Abstract

This paper focuses on providing a countermeasure to replay attack which is the simplest and more accessible form of attack used to spoof automatic speaker verification systems. Specifically, it proposes the use of the transmission line cochlear model, which resembles the human cochlea more accurately than parallel filter bank models, in the front-end of replay detection systems. Here the basilar membrane is modeled as a cascade of digital filters with decreasing resonant frequencies. In this context, we propose two features - transmission line cochlea-amplitude modulation (TLC-AM) and frequency modulation (TLC-FM) - to extract the modulation features of the speech from the simulated membrane displacements. TLC-AM is analogous to the output of the inner hair cell bending movement, which accurately captures the amplitude modulation component of the speech. TLC-FM is extracted by deriving the in-phase and out of phase signals of basilar membrane displacement. Results show that individual TLC-AM and TLC-FM features perform better than the best parallel filter bank baseline system. Experiments suggest that higher frequency selectivity is beneficial for replay detection, especially for AM, and the proposed TLC model is better able to achieve this property than parallel filter bank models.