Abstract: A method of training a machine learning model to conduct authentication transactions is provided that includes the steps of obtaining, by an electronic device, a training dataset of audio signals. Each audio signal includes voice biometric data of a user and information for a passphrase spoken by the respective user and belongs to a same or different data class. Each data class includes a user identity and a passphrase identifier. Moreover, the method includes the steps of creating, using a machine learning model being trained, at least one embedding for each audio signal. The machine learning model includes parameters. Furthermore, the method includes calculating, by a machine learning algorithm using the embeddings, a loss, and updating parameters of the machine learning model based on the calculated loss.

Abstract: A method of training a machine learning model (MLM) to conduct authentication transactions includes obtaining, by an electronic device, a training dataset of audio signals. Each audio signal includes voice biometric data of a user and an audio data production flag and belongs to a same or different data class. Moreover, the method includes creating, using a MLM being trained, at least one embedding for each audio signal and calculating, using the at least one embedding, a loss for a machine learning algorithm used for training the MLM. Furthermore, the method includes updating parameters of the MLM based on the calculated loss. In response to determining criteria defining an end of training have been satisfied, deeming the MLM to be operable for use in simultaneously successfully verifying the identity of a user based on voice biometric data and verifying the voice biometric data was spoken by a living person.

Abstract: A biometric system is tested to see whether a proposed use matches a configuration of the system. An enrolment input is received from an enrolling user, and compared with a system configuration model to obtain a configuration matching score value. The enrollment is then controlled based on a result of comparing the received enrollment input with the system configuration model. In the case of a voice biometric system, when a test input is received from a speaker, it is determined whether audio conditions applying to the test input correspond to system configuration conditions. Verification is performed by comparing the test input with a model of the speech of an enrolled user to generate a verification score for use in deciding whether to accept or reject the speaker, depending on whether it is determined that audio conditions applying to the test input correspond to the system configuration conditions.

Abstract: A biometric system is tested to see whether a proposed use matches a configuration of the system. An enrolment input is received from an enrolling user, and compared with a system configuration model to obtain a configuration matching score value. The enrolment is then controlled based on a result of comparing the received enrolment input with the system configuration model. In the case of a voice biometric system, when a test input is received from a speaker, it is determined whether audio conditions applying to the test input correspond to system configuration conditions. Verification is performed by comparing the test input with a model of the speech of an enrolled user to generate a verification score for use in deciding whether to accept or reject the speaker, depending on whether it is determined that audio conditions applying to the test input correspond to the system configuration conditions.