Embodiments of the present invention provide systems and methods for using machine learning to analyze and infer the contextual significance of a conversational language in order to proactively engage with one or more users in a familiar manner via a virtual voice assistant. As such, the systems and methods reduce redundancy of process steps for the user in accessing relevant information or initiating certain resource activities via disparate channels of communication by creating a continuity of conversational tone and substance.

A conference recording method is provided. The method includes obtaining a multimedia file corresponding to a conference, the multimedia file includes video data and audio data. Posture language of each person is recognized from the video data. Facial features and voice features of each person are extracted from the multimedia file. Personal identity information of each person is identified according to the facial features and the voice features of each person. Once the audio data corresponding to each person is converted into text information, the posture language, the personal identity information, and the text information corresponding to each person are output.

A method detecting passenger specific voice data based on vehicle operating conditions that are set based on the passenger specific voice data are provided. The method includes obtaining, using a sensor operating in association with the computing device, image data associated with a passenger in the vehicle, obtaining an identification of the passenger based on the image data, and retrieving one or more voice characteristics of the passenger based on the identification. The method also includes selecting, by the computing device, an operating condition for an additional sensor of the vehicle based on the one or more voice characteristics of the passenger, and detecting, by the additional sensor that operates in the operating condition, voice data specific to the passenger.

A method detecting passenger specific voice data based on vehicle operating conditions that are set based on the passenger specific voice data are provided. The method includes obtaining, using a sensor operating in association with the computing device, image data associated with a passenger in the vehicle, obtaining an identification of the passenger based on the image data, and retrieving one or more voice characteristics of the passenger based on the identification. The method also includes selecting, by the computing device, an operating condition for an additional sensor of the vehicle based on the one or more voice characteristics of the passenger, and detecting, by the additional sensor that operates in the operating condition, voice data specific to the passenger.

The embodiments execute machine-learning architectures for biometric-based identity recognition (e.g., speaker recognition, facial recognition) and deepfake detection (e.g., speaker deepfake detection, facial deepfake detection). The machine-learning architecture includes layers defining multiple scoring components, including sub-architectures for speaker deepfake detection, speaker recognition, facial deepfake detection, facial recognition, and lip-sync estimation engine. The machine-learning architecture extracts and analyzes various types of low-level features from both audio data and visual data, combines the various scores, and uses the scores to determine the likelihood that the audiovisual data contains deepfake content and the likelihood that a claimed identity of a person in the video matches to the identity of an expected or enrolled person. This enables the machine-learning architecture to perform identity recognition and verification, and deepfake detection, in an integrated fashion, for both audio data and visual data.

Embodiments of the invention relate to methods, apparatus and systems for biometric processes. The methods include updating stored ear model data for a user following successful authentication of the user. The ear model data may be acquired using a personal audio device that generates an acoustic stimulus and detects a measured response. The acquisition of the ear model data may be responsive to a determination that the personal audio device is inserted into or placed adjacent to the user's ear. The acquisition of the ear model data may also be responsive to the determination that the personal audio device has not been removed from or moved away from the user's ear.

A device may receive a command associated with identifying a merchant for a virtual card swap procedure wherein the virtual card swap procedure is to replace a credit card of a user with a virtual card corresponding to the credit card. The device may identify the merchant for the virtual card swap procedure based on the command. The device may obtain the virtual card for the user. The device may determine a virtual card swap procedure template for the merchant. The device may perform the virtual card swap procedure based on the virtual card swap procedure template.

This application relates to microphone authentication apparatus for verifying whether or not an audio signal originated at a microphone. The microphone authentication apparatus has a comparison block configured to receive a first signal indicative of one or more spectral parameters of at least part of an audio signal to be verified, and compare the one or more spectral parameters to one or more predetermined characteristic microphone parameters relating to a characteristic resonance associated with an acoustic port of a microphone. The first signal may be an audio signal and the microphone authentication apparatus may have a feature extract module for determining the spectral parameters. Based on the comparison determination block may whether the audio signal originated from a microphone and may send a verification signal to a voice biometric module.

A system and method for identifying a user of a device includes comparing audio received by a device with acoustic fingerprint information to identify a user of the device. Image data, video data and other data may also be used in the identification of the user. Once the user is identified, operation of the device may be customized based on the user. Further, once the user is identified, data can be associated with the user, for example, usage data, location data, gender data, age data, dominant hand data of the user, and other data. This data can then be used to further customize the operation of the device to the specific user.

A system and method for identifying a user of a device includes comparing audio received by a device with acoustic fingerprint information to identify a user of the device. Image data, video data and other data may also be used in the identification of the user. Once the user is identified, operation of the device may be customized based on the user. Further, once the user is identified, data can be associated with the user, for example, usage data, location data, gender data, age data, dominant hand data of the user, and other data. This data can then be used to further customize the operation of the device to the specific user.