AM and LM parameters to be used for adapting an ASR model are derived for each audio segment of an audio stream comprising multiple audio programs. A set of identifiers, including a speaker identifier, a speaker domain identifier and a program domain identifier, is obtained for each audio segment. The set of identifiers are used to select most suitable AM and LM parameters for the particular audio segment. The embodiments enable provision of maximum constraints on the AMs and LMs and enable adaptation of the ASR model on the fly for audio streams of multiple audio programs, such as broadcast audio. This means that the embodiments enable selecting AM and LM parameters that are most suitable in terms of ASR performance for each audio segment.

An electronic device according to various embodiments of the disclosure may include: a microphone, at least one sensor, a memory storing a speaker verification model for verifying a voice of a registered user, and at least one processor operatively connected to the microphone, the at least one sensor, and the memory, wherein the at least one processor is configured to: identify whether user verification information is received through the at least one sensor within a designated time interval before or after a time point at which an uttered voice is received based on receiving the uttered voice through the microphone, and updates the speaker verification model using the uttered voice based on the user verification being completed according to the user verification information.

An electronic device according to various embodiments of the disclosure may include: a microphone, at least one sensor, a memory storing a speaker verification model for verifying a voice of a registered user, and at least one processor operatively connected to the microphone, the at least one sensor, and the memory, wherein the at least one processor is configured to: identify whether user verification information is received through the at least one sensor within a designated time interval before or after a time point at which an uttered voice is received based on receiving the uttered voice through the microphone, and updates the speaker verification model using the uttered voice based on the user verification being completed according to the user verification information.

Methods and systems are disclosed herein for improving the quality of audio for use in a biometric. A biometric system may use machine learning to determine whether audio or a portion of the audio should be used as a biometric for a user. A sample of the user's voice may be used to generate a voice signature of the user. Portions of the audio that do not meet a similarity threshold when compared with the voice signature may be removed from the audio. Additionally or alternatively, interfering noises may be detected and removed from the audio to improve the quality of a voice biometric generated from the audio.

Methods and systems are disclosed herein for improving the quality of audio for use in a biometric. A biometric system may use machine learning to determine whether audio or a portion of the audio should be used as a biometric for a user. A sample of the user's voice may be used to generate a voice signature of the user. Portions of the audio that do not meet a similarity threshold when compared with the voice signature may be removed from the audio. Additionally or alternatively, interfering noises may be detected and removed from the audio to improve the quality of a voice biometric generated from the audio.

A system for selectively amplifying audio signals may include a microphone configured to capture sounds from an environment of a user. The system may also include a processor programmed to: receive audio signals representative of the sounds captured by the microphone; cause selective conditioning of at least one audio signal received by the microphone from a region associated with the recognized individual; and cause transmission of the at least one conditioned audio signal to a hearing interface device configured to provide sound to an ear of the user.

A system for selectively amplifying audio signals may include a microphone configured to capture sounds from an environment of a user. The system may also include a processor programmed to: receive audio signals representative of the sounds captured by the microphone; cause selective conditioning of at least one audio signal received by the microphone from a region associated with the recognized individual; and cause transmission of the at least one conditioned audio signal to a hearing interface device configured to provide sound to an ear of the user.

A system is provided for real-time fraud detection with a fraudster voice print watchlist of repetitive fraudster phrases. The system includes a processor and a computer readable medium operably coupled thereto, to perform fraud prevention operations which include detecting a voice communication session having an audio signal of a user, accessing the fraudster voice print watchlist comprising a plurality of fraudster voice prints of the repetitive fraudster phrases, generating a voice print of the user using the audio signal, monitoring the user for real-time fraud detection using the fraudster voice print watchlist and the voice print of the user, and determining, based on the monitoring, whether the voice print of the user meets or exceeds a scoring threshold for matching with one or more of the plurality of fraudster voice prints from the fraudster voice print watchlist during the voice communication session.

A system is provided for real-time fraud detection with a fraudster voice print watchlist of repetitive fraudster phrases. The system includes a processor and a computer readable medium operably coupled thereto, to perform fraud prevention operations which include detecting a voice communication session having an audio signal of a user, accessing the fraudster voice print watchlist comprising a plurality of fraudster voice prints of the repetitive fraudster phrases, generating a voice print of the user using the audio signal, monitoring the user for real-time fraud detection using the fraudster voice print watchlist and the voice print of the user, and determining, based on the monitoring, whether the voice print of the user meets or exceeds a scoring threshold for matching with one or more of the plurality of fraudster voice prints from the fraudster voice print watchlist during the voice communication session.

A method, computer program product, and computing system for generating a generative model representative of a plurality of natural biometric profiles. A plurality of random samples are generated from the generative model. A plurality of synthetic biometric profiles are generated based upon, at least in part, the plurality of random samples.