1 research outputs found
Spoofing Detection in Voice Biometrics: Cochlear Modelling and Perceptually Motivated Features
The automatic speaker verification (ASV) system is one of the most widely adopted biometric
technology. However, ASV is vulnerable to spoofing attacks that can significantly affect its
reliability. Among the different variants of spoofing attacks, replay attacks pose a major threat as
they do not require any expert knowledge to implement and are difficult to detect. The primary focus
of this thesis is on understanding and developing biologically inspired models and techniques to
detect replay attacks.
This thesis develops a novel framework for implementing an active cochlear filter model as a frontend
spectral analyser for spoofing attack detection to leverage the remarkable sensitivity and
selectivity of the mammalian auditory system over a broad range of intensities and frequencies. In
particular, the developed model aims to mimic the active mechanism in the cochlea, enabling sharp
frequency tuning and level-dependent compression, which amplifies and tune to low energy signal
to make a broad dynamic range of signals audible. Experimental evaluations of the developed models
in the context of replay detection systems exhibit a significant performance improvement,
highlighting the potential benefits of the use of biologically inspired front ends.
In addition, since replay detection relies on the discerning channel characteristics and the effect of
the acoustic environment, acoustic cues essential for speech perception such as amplitude- and
frequency-modulation (AM, FM) features are also investigated. Finally, to capture discriminative
cues present in the temporal domain, the temporal masking psychoacoustic phenomenon in auditory
processing is exploited, and the usefulness of the masking pattern is investigated. This led to a novel
feature parameterisation which helps improve replay attack detection