Detecting deep-fake audio through vocal tract reconstruction

A method comprising:

• generating a model of a vocal tract based at least in part on frequency response of one or more organic audio samples from a user;
• identifying a set of bigram-feature pairs from the one or more organic audio samples;
• estimating, from the generated model of the vocal tract, the cross-sectional area of the vocal tract of the user when speaking the set of bigram-feature pairs;
• receiving a candidate audio sample;
• identifying bigram-feature pairs of the candidate audio sample that are in the set of bigram-feature pairs;
• calculating a cross-sectional area of a theoretical vocal tract of a user when speaking the identified bigram-feature pairs of the candidate audio sample;
• and
• identifying the candidate audio sample as a deep-fake audio sample in response to the calculated cross-sectional area of the theoretical vocal tract of a user when speaking the identified bigram-feature pairs of the candidate audio sample failing to correspond within a predefined measure of the estimated cross-sectional area of the vocal tract of the user when speaking the set of bigram-feature pairs.

A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code portions stored therein, the computer-executable program code portions comprising program code instructions configured to:

• generate a model of a vocal tract based at least in part on frequency response of one or more organic audio samples from a user;
• identify a set of bigram-feature pairs from the one or more organic audio samples;
• estimate, from the generated model of the vocal tract, the cross-sectional area of the vocal tract of the user when speaking the set of bigram-feature pairs;
• receive a candidate audio sample;
• identify bigram-feature pairs of the candidate audio sample that are in the set of bigram-feature pairs;
• calculate a cross-sectional area of a theoretical vocal tract of a user when speaking the identified bigram-feature pairs of the candidate audio sample;
• and
• identify the candidate audio sample as a deep-fake audio sample in response to the calculated cross-sectional area of the theoretical vocal tract of a user when speaking the identified bigram-feature pairs of the candidate audio sample failing to correspond within a predefined measure of the estimated cross-sectional area of the vocal tract of the user when speaking the set of bigram-feature pairs.