Daon Technology

Davyd Mizynchuk

Raphael A Rodriguez

Olena Mizynchuk

A method for enhancing detection of synthetic speech is provided that includes the step of receiving, by an electronic device, voice biometric data of a user captured while the user was speaking and analyzing the context in which the received voice biometric data was captured. The context includes environmental and situational factors. Moreover, the method includes the steps of analyzing characteristics of the received voice biometric data for anomalies associated with synthetic speech, generating a risk score based on the results of the analysis, and comparing the risk score against a threshold value. In response to determining the risk score fails to satisfy the threshold score, the method includes a step of determining the captured voice biometric data includes anomalies associated with synthetic speech and initiating an alert protocol.

Methods and systems for enhancing the detection of synthetic speech

A method for enhancing detection of synthetic voice data is provided that includes converting, by an electronic device, monophonic voice data into stereophonic voice data. The stereophonic voice data includes a first channel signal and a second channel signal. Moreover, the method includes decomposing, by a trained machine learning model, the stereophonic voice data into a mid-signal and a side signal. The method also includes determining artifacts indicative of synthetic generation in the structured and secondary artifacts, calculating, based on the determined artifacts, a probability score reflecting the likelihood the monophonic voice data was synthetically generated, and comparing the probability score against a threshold value. When the probability score satisfies the threshold value, there is a high likelihood that the monophonic voice data includes synthetic artifacts, and an alert is generated indicating the monophonic voice data is potentially fraudulent.

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Kyiv, Ukraine

Davyd Mizynchuk