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.

A microphone device, comprising: a microphone configured to generate an audio signal; a natural speech detection module for detecting natural speech in the first audio signal; wherein, on detection of the natural speech in the first audio signal, the natural speech detection module is configured to output a trigger signal to a speech processing module to process the natural speech in the audio signal.

A microphone device, comprising: a microphone configured to generate an audio signal; a natural speech detection module for detecting natural speech in the first audio signal; wherein, on detection of the natural speech in the first audio signal, the natural speech detection module is configured to output a trigger signal to a speech processing module to process the natural speech in the audio signal.

A method, the method comprising retrieving a sound intensity map for a venue, wherein the sound intensity map is divided up into a plurality of regions, wherein the sound intensity map predicts a sound quality for each region during a current event. Receiving data from a plurality of IOT enabled operated aerial vehicles, where each IOT enabled operated aerial vehicle of the plurality of IOT enabled operated aerial vehicles travels around different regions of the plurality of regions, wherein each IOT enabled operated aerial vehicle collects data during the event. Comparing the received data to the sound intensity map to determine the region where an audio component of a venue audio needs to be adjusted. Determining the adjustment required for the audio component and adjusting the audio equipment.

A method, the method comprising retrieving a sound intensity map for a venue, wherein the sound intensity map is divided up into a plurality of regions, wherein the sound intensity map predicts a sound quality for each region during a current event. Receiving data from a plurality of IOT enabled operated aerial vehicles, where each IOT enabled operated aerial vehicle of the plurality of IOT enabled operated aerial vehicles travels around different regions of the plurality of regions, wherein each IOT enabled operated aerial vehicle collects data during the event. Comparing the received data to the sound intensity map to determine the region where an audio component of a venue audio needs to be adjusted. Determining the adjustment required for the audio component and adjusting the audio equipment.

The present invention provides a deep learning based method and system for processing sound quality characteristics. The method comprises: obtaining data characteristics of an audio data to be processed by extracting features from user preference data including the audio data to be processed; based on the data characteristics, generating a sound quality processing result of the audio to be processed by using a trained baseline model; wherein the baseline model is a neural network model trained by using audio data behavioral data, and other relevant data from multiple users or a single user.

The present invention provides a deep learning based method and system for processing sound quality characteristics. The method comprises: obtaining data characteristics of an audio data to be processed by extracting features from user preference data including the audio data to be processed; based on the data characteristics, generating a sound quality processing result of the audio to be processed by using a trained baseline model; wherein the baseline model is a neural network model trained by using audio data behavioral data, and other relevant data from multiple users or a single user.

Systems and methods for assessing the visual acuity of person using a computerized consumer device are described. The approach involves determining a separation distance between a human user and the consumer device based on an image size of a physical feature of the user, instructing the user to adjust the separation between the user and the consumer device until a predetermined separation distance range is achieved, presenting a visual acuity test to the user including displaying predetermined optotypes for identification by the user, recording the user's spoken identifications of the predetermined optotypes and providing real-time feedback to the user of detection of the spoken indications by the consumer device, carrying out voice recognition on the spoken identifications to generate corresponding converted text, comparing recognized words of the converted text to permissible words corresponding to the predetermined optotypes, determining a score based on the comparison, and determining whether the person passed the visual acuity test.

A stable evaluation result is obtained from a voice of speech for any sentence. A speech evaluation device (1) outputs a score for evaluating speech of an input voice signal spoken by a speaker in a first group. A feature extraction unit (11) extracts an acoustic feature from the input voice signal. A conversion unit (12) converts the acoustic feature of the input voice signal to an acoustic feature when a speaker in a second group speaks the same text as text of the input voice signal. An evaluation unit (13) calculates a score indicating a higher evaluation as a distance between the acoustic feature before the conversion and the acoustic feature after the conversion becomes shorter.