  Recent advances in artificial speech and audio technologies have improved the
abilities of deep-fake operators to falsify media and spread malicious
misinformation. Anyone with limited coding skills can use freely available
speech synthesis tools to create convincing simulations of influential
speakers' voices with the malicious intent to distort the original message.
With the latest technology, malicious operators do not have to generate an
entire audio clip; instead, they can insert a partial manipulation or a segment
of synthetic speech into a genuine audio recording to change the entire context
and meaning of the original message. Detecting these insertions is especially
challenging because partially manipulated audio can more easily avoid synthetic
speech detectors than entirely fake messages can. This paper describes a
potential partial synthetic speech detection system based on the x-ResNet
architecture with a probabilistic linear discriminant analysis (PLDA) backend
and interleaved aware score processing. Experimental results suggest that the
PLDA backend results in a 25% average error reduction among partially
synthesized datasets over a non-PLDA baseline.
