DEEPFAKE DETECTION
Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.
1 . A computer-implemented method for detecting machine-based speech in calls, the method comprising:
obtaining, by a computer, a plurality of audio speech signals for a caller from a caller device corresponding to a plurality of instances of a repeated audible prompt provided to the caller device;
for each instance of the repeated audible prompt, extracting, by the computer, a set of acoustic features from the audio speech signal;
determining, by the computer, a similarity score for the plurality of audio speech signals based upon each set of acoustic features extracted from each corresponding audio speech signals; and
identifying, by the computer, the caller as a fraudulent caller in response to determining that the similarity score satisfies a similarity threshold.
2 . The method of claim 1 , further comprising transmitting, by the computer, each instance of the repeat audible prompt to the caller device.
3 . The method of claim 1 , wherein an interactive voice response (IVR) program generates the repeated audible prompt for transmission to the caller device.
4 . The method of claim 1 , wherein the acoustic features include content of the audio speech signal recognized by a machine-learning architecture executed by the computer.
5 . The method of claim 1 , wherein the acoustic features include low-level acoustic features of the audio speech signal recognized by a machine-learning architecture executed by the computer.
6 . The method of claim 1 , wherein the acoustic features include speech patterns of the caller in the audio speech signal recognized by a machine-learning architecture executed by the computer.
7 . The method of claim 1 , further comprising applying, by the computer, a dynamic time-warping (DTW) function on a first speech signal and a second speech signal of the plurality of audio speech signals to determine the similarity score between the first speech signal and the second speech signal.
8 . The method of claim 1 , further comprising applying, by the computer, a neural network architecture on a first speech signal and a second speech signal of the plurality of audio speech signals to determine the similarity score between the first speech signal and the second speech signal.
9 . The method of claim 1 , further comprising applying, by the computer, a speaker embedding neural network to extract a speaker embedding using the set of acoustic features from each audio speech signal, and to generate the similarity score by comparing each speaker embedding.
10 . The method of claim 1 , further comprising providing, by the computer, via a user interface, an indication of the caller classified as one of the fraudulent or genuine.
11 . A system for detecting machine-based speech in calls, comprising:
a computer comprising one or more processors and configured to:
obtain a plurality of audio speech signals for a caller from a caller device corresponding to a plurality of instances of a repeated audible prompt provided to the caller device;
for each instance of the repeated audible prompt, extracting, by the computer, a set of acoustic features from the audio speech signal;
determine a similarity score for the plurality of audio speech signals based upon each set of acoustic features extracted from each corresponding audio speech signals; and
identify the caller as a fraudulent caller in response to determining that the similarity score satisfies a similarity threshold.
12 . The system of claim 11 , wherein the computer is further configured to transmit each instance of the repeat audible prompt to the caller device.
13 . The system of claim 11 , further comprising at least one computer having a machine-executed interactive voice response (IVR) program configured to generate the repeated audible prompt for transmission to the caller device.
14 . The system of claim 11 , wherein the acoustic features include content of the audio speech signal recognized by a machine-learning architecture executed by the computer.
15 . The system of claim 11 , wherein the acoustic features include low-level acoustic features of the audio speech signal recognized by a machine-learning architecture executed by the computer.
16 . The system of claim 11 , wherein the acoustic features include speech patterns of the caller in the audio speech signal recognized by a machine-learning architecture executed by the computer.
17 . The system of claim 11 , wherein the computer is further configured to apply a dynamic time-warping (DTW) function on a first speech signal and a second speech signal of the plurality of audio speech signals to determine the similarity score between the first speech signal and the second speech signal.
18 . The system of claim 11 , wherein the computer is further configured to apply a neural network architecture on a first speech signal and a second speech signal of the plurality of audio speech signals to determine the similarity score between the first speech signal and the second speech signal.
19 . The system of claim 11 , wherein the computer is further configured to extract a speaker embedding using the set of acoustic features from each audio speech signal; and wherein the computer generates the similarity score by comparing each speaker embedding.
20 . The system of claim 11 , wherein the computer is further configured to provide, via a user interface, an indication of the caller classified as one of the fraudulent or genuine.