A one-class classification approach to generalised speaker verification spoofing countermeasures using local binary patterns

The vulnerability of automatic speaker verification systems to spoofing is now well accepted. While recent work has shown the potential to develop countermeasures capable of detecting spoofed speech signals, existing solutions typically function well only for specific attacks on

which they are optimised. Since the exact nature of spoofing attacks can never be known in practice, there is thus a need for generalised countermeasures which can detect previously unseen spoofing attacks. This paper presents a novel countermeasure based on the analysis of speech signals using local binary patterns followed by a one-class classification approach. The new countermeasure captures differences in the spectro-temporal texture of genuine and

spoofed speech, but relies only on a model of the former. We report experiments with three different approaches to spoofing and with a state-of-the-art i-vector speaker verification

system which uses probabilistic linear discriminant analysis for intersession compensation. While a support vector machine classifier is tuned with examples of converted voice, it delivers reliable detection of spoofing attacks using synthesized speech and artificial signals, attacks for which it is

not optimised.