Robust spoofing detection system using deep residual neural networks

A computer-implemented method for spoofing countermeasures, the method comprising:

• obtaining, by a computer, a plurality of training audio signals including one or more clean audio signals and one or more simulated audio signals;
• training, by the computer, a neural network architecture to extract a spoofprint embedding from an audio signal and classify the audio signal, the neural network architecture trained by applying the neural network architecture on a plurality of features of the plurality of training audio signals;
• extracting, by the computer, an inbound spoofprint for the inbound speaker by applying the neural network architecture on the plurality of features of an inbound audio signal;
• and
• generating, by the computer, a classification for the inbound audio signal based upon applying the neural network architecture on the inbound spoofprint.

A system comprising:

• a non-transitory machine readable memory;
• and
• a computer comprising a processor configured to:
• generate an enrollee spoofprint for an enrollee based upon a first set of one or more features extracted from one or more enrollee audio signals for the enrollee, wherein the first set of one or more features includes one or more types of audio characteristics of the enrollee;
• store the enrollee spoofprint into the memory;
• apply a neural network architecture to an inbound audio signal, the neural network architecture trained to detect spoofing artifacts occurring in an audio signal;
• generate an inbound spoofprint for an inbound speaker by applying the neural network architecture to an inbound audio signal for the inbound speaker;
• and
• generate a spoof likelihood score for the inbound audio signal based upon one or more similarities between the inbound spoofprint and the enrollee spoofprint.