A method for protecting a biometric template, comprising the steps of: retrieving an original vector (V) representing said biometric template, said vector comprising a plurality of original elements (v.sub.1, v.sub.2, . . . v.sub.i, . . . , v.sub.n); mapping at least some elements from said original vector to a protected vector (P) comprising a plurality of protected elements (p.sub.1, p.sub.2, . . . p.sub.i, . . . , p.sub.n−m+1), the mapping being based on multivariate polynomials defined by m user-specific coefficients (C) and exponents (E).

A method for protecting a biometric template, comprising the steps of: retrieving an original vector (V) representing said biometric template, said vector comprising a plurality of original elements (v.sub.1, v.sub.2, . . . v.sub.i, . . . , v.sub.n); mapping at least some elements from said original vector to a protected vector (P) comprising a plurality of protected elements (p.sub.1, p.sub.2, . . . p.sub.i, . . . , p.sub.n−m+1), the mapping being based on multivariate polynomials defined by m user-specific coefficients (C) and exponents (E).

A user authentication method in a messaging application of an electronic device. The method comprises, if at least one text message is typed by a user in the messaging application, collecting image data relating to said user and behavioral data relating to said user, and, if at least one voice message is pronounced by said user in the messaging application, collecting image data relating to said user and voice data relating to said user. The method also comprises, depending on the type of the message from text messages and voice messages, determining an image recognition score based upon comparison of the collected image data relating to said user and a stored image template data relating to said user obtained during typing or pronouncing a message by said user during a prior session, determining a voice recognition score based upon comparison of the collected voice data relating to said user and a stored voice template data relating to said user obtained during pronouncing a message by said user during a prior session, and determining a behavioral recognition score based upon comparison of the collected behavioral data relating to said user and a stored behavioral template data relating to said user obtained when said user typed the message during a prior session. The method also comprises creating a biometric score by using fusion of the image recognition score and one of the voice recognition score and the behavioral recognition score, and authenticating said user using the biometric score. Present invention allows to authenticate users in messaging applications or virtual assistants during typing and pronunciation of a message with high degree of accuracy.

A user authentication method in a messaging application of an electronic device. The method comprises, if at least one text message is typed by a user in the messaging application, collecting image data relating to said user and behavioral data relating to said user, and, if at least one voice message is pronounced by said user in the messaging application, collecting image data relating to said user and voice data relating to said user. The method also comprises, depending on the type of the message from text messages and voice messages, determining an image recognition score based upon comparison of the collected image data relating to said user and a stored image template data relating to said user obtained during typing or pronouncing a message by said user during a prior session, determining a voice recognition score based upon comparison of the collected voice data relating to said user and a stored voice template data relating to said user obtained during pronouncing a message by said user during a prior session, and determining a behavioral recognition score based upon comparison of the collected behavioral data relating to said user and a stored behavioral template data relating to said user obtained when said user typed the message during a prior session. The method also comprises creating a biometric score by using fusion of the image recognition score and one of the voice recognition score and the behavioral recognition score, and authenticating said user using the biometric score. Present invention allows to authenticate users in messaging applications or virtual assistants during typing and pronunciation of a message with high degree of accuracy.

Disclosed is a personal hearing device, an external acoustic processing device and an associated computer program product. The personal hearing device includes: a microphone, for receiving an input acoustic signal, wherein the input acoustic signal is a mixture of sounds coming from a first acoustic source and from other acoustic source(s); a speaker; and an acoustic processing circuit, for automatically distinguishing within the input acoustic signal the sound of the first acoustic source from the sound of other acoustic source(s); wherein the acoustic processing circuit further processes the input acoustic signal by having different modifications to the sound of the first acoustic source and to the sound of other acoustic source(s), whereby the acoustic processing circuit produces an output acoustic signal to be played on the speaker.

There are disclosed methods and systems for determining a speaker of a set of registered users associated with a voice-controllable device. The method is executable by an electronic device configured to execute a Machine Learning Algorithm (MLA). The method comprises executing the MLA to determine a first probability parameter indicative of the speaker of the user utterance being one of the set of registered users; executing a user frequency analysis to generate, for each given one of the set of registered users, a second probability parameter the being an apriori frequency based probability; generating, for the electronic device, for each given one of the set of registered users an amalgamated probability based on the first probability and the second probability associated therewith; selecting the given one of the set of registered users as the speaker of the user utterance based on the amalgamated probability value.

There are disclosed methods and systems for determining a speaker of a set of registered users associated with a voice-controllable device. The method is executable by an electronic device configured to execute a Machine Learning Algorithm (MLA). The method comprises executing the MLA to determine a first probability parameter indicative of the speaker of the user utterance being one of the set of registered users; executing a user frequency analysis to generate, for each given one of the set of registered users, a second probability parameter the being an apriori frequency based probability; generating, for the electronic device, for each given one of the set of registered users an amalgamated probability based on the first probability and the second probability associated therewith; selecting the given one of the set of registered users as the speaker of the user utterance based on the amalgamated probability value.

An electronic device is provided, which includes a user interface, at least one communication module, a microphone, at least one speaker, at least one processor operatively connected with the user interface, the at least one communication module, the microphone, and the at least one speaker, and at least one memory operatively connected with the at least one processor, wherein the at least one memory stores instructions, which when executed, instruct the at least one processor to while the electronic device is wiredly or wirelessly connected with an access point (AP) connected with at least one external electronic device, after receiving, through the microphone, part of a wake-up utterance to invoke a voice-based intelligent assistant service, broadcast identification information about the electronic device and receive identification information broadcast from the external electronic device, after receiving the whole wake-up utterance through the microphone, individually transmit first information related to the wake-up utterance received through the microphone to the at least one external electronic device and individually receive, from the external electronic device, second information related to the wake-up utterance received by the at least one external electronic device, and determine whether to transmit voice information received after the wake-up utterance to an external server based on at least part of the first information and the second information. Other various embodiments are possible as well.

Vehicle manipulation is performed using crowdsourced data. A camera within a vehicle is used to collect cognitive state data, including facial data, on a plurality of occupants in a plurality of vehicles. A first computing device is used to learn a plurality of cognitive state profiles for the plurality of occupants, based on the cognitive state data. The cognitive state profiles include information on an absolute time or a trip duration time. Voice data is collected and is used to augment the cognitive state data. A second computing device is used to capture further cognitive state data on an individual occupant in an individual vehicle. A third computing device is used to compare the further cognitive state data with the cognitive state profiles that were learned. The individual vehicle is manipulated based on the comparing of the further cognitive state data.

Vehicle manipulation is performed using crowdsourced data. A camera within a vehicle is used to collect cognitive state data, including facial data, on a plurality of occupants in a plurality of vehicles. A first computing device is used to learn a plurality of cognitive state profiles for the plurality of occupants, based on the cognitive state data. The cognitive state profiles include information on an absolute time or a trip duration time. Voice data is collected and is used to augment the cognitive state data. A second computing device is used to capture further cognitive state data on an individual occupant in an individual vehicle. A third computing device is used to compare the further cognitive state data with the cognitive state profiles that were learned. The individual vehicle is manipulated based on the comparing of the further cognitive state data.