Agnitio Sl

Cirrus Logic Inc.

Cirrus Logic International Semiconductor Limited

Identy Inc.

Marta Garcia Gomar

Luis Buera Rodriguez

Carlos Vaquero Aviles-Casco

Hardik Gupta

Johan Nikolaas Langehoven Brummer

Jesus Antonio Villalba Lopez

Daniel Erro

Eduardo Lleida Solano

Jesús Antonio Villalba López

Sara Varela Redondo

Antonio Miguel Artiaga

Alfonso Ortega Gimenez

Marta Sánchez Asenjo

Luiz Buera Rodriguez

Alfonso Ortega GIMÉNEZ

Alberto Martin De Los Santos De Las Heras

Alfredo Gutierrez

Method for resizing an image, the method comprising the steps: obtaining an image of a user comprising at least one biometric identifier, wherein the biometric identifier comprises a plurality of biometric characteristics; determining a first frequency of at least part of the plurality of the biometric characteristics in the image; determining a second frequency based on the first frequency and a property of the biometric identifier; resizing the image based on a ratio of the first and the second frequency; obtaining a resized image.

Method for resizing an image

A speaker recognition system for authenticating a mobile device user includes an enrollment and learning software module, a voice biometric authentication software module, and a secure software application. Upon request by a user of the mobile device, the enrollment and learning software module displays text prompts to the user, receives speech utterances from the user, and produces a voice biometric print. The enrollment and training software module determines when a voice biometric print has met at least a quality threshold before storing it on the mobile device. The secure software application prompts a user requiring authentication to repeat an utterance based at least on an attribute of a selected voice biometric print, receives a corresponding utterance, requests the voice biometric authentication software module to verify the identity of the second user using the utterance, and, if the user is authenticated, imports the voice biometric print.

System and method for speaker recognition on mobile devices

A tamper-resistant element for use in speaker recognition, the tamper-resistant element being adapted for storing data representing speaker information based on speaker recognition enrollment data and for checking whether information based on a speaker recognition testing signal matches the speaker information, characterized in that the tamper-resistant element is also adapted for carrying out a data integrity check, a system comprising such a tamper-resistant element and method for speaker recognition.

Tamper-resistant element for use in speaker recognition

The invention refers to a method of verifying the identity of a speaker based on the speakers voice comprising the steps of: receiving () a first and a second voice utterance; using biometric voice data to verify () that the speakers voice corresponds to the speaker the identity of which is to be verified based on the received first and/or second voice utterance and determine () the similarity of the two received voice utterances characterized in that the similarity is determined using biometric voice characteristics of the two voice utterances or data derived from such biometric voice characteristics. The invention further refers to a System () for verifying the identity of a speaker based on the speakers voice comprising: a component () for receiving a first and a second voice utterance; a component () for using biometric voice data to verify that the speakers voice corresponds to the speaker the identity of which is to be verified based on the received first and/or second voice utterance and a component () for comparing the two received voice utterances in order to determine the similarity of the two voice utterances characterized in that the similarity is determined using biometric voice characteristics of the two voice utterances or data derived from such biometric voice characteristics.

Method for verifying the identity of a speaker, system therefore and computer readable medium

A method for speaker recognition comprising: obtaining speaker information for a target speaker; obtaining speech samples from telephone calls from an unknown speaker; classifying the speech samples according the unknown speaker thereby providing speaker-dependent classes of speech samples; extracting speaker information of each of the speaker-dependent classes of speech samples; combining the extracted speaker information; comparing the combined extracted speaker information with the stored speaker information for the target speaker to obtain a comparison result; and determining whether the unknown speaker is identical with the target speaker based on the comparison result.

Speaker recognition from telephone calls

Method for text-dependent Speaker Recognition using a speaker adapted Universal Background Model, wherein the speaker adapted Universal Background Model is a speaker adapted Hidden Markov Model comprising channel correction.

Speaker recognition

A tamper-resistant element for use in speaker recognition, the tamper-resistant element being adapted for storing data representing speaker information based on speaker recognition enrollment data and for checking whether information based on a speaker recognition testing signal matches the speaker information. The tamper-resistant element is also adapted for carrying out a data integrity check. Also, a system including such a tamper-resistant element and method for speaker recognition.

Method for speaker identification includes detecting a target speaker's utterance locally; extracting features from the detected utterance locally, analyzing the extracted features in the local device to obtain information on the speaker identification and/or encoding the extracted features locally, transmitting the encoded extracted features to a remote server, decoding and analyzing the received extracted features by the server to obtain information on the speaker identification, and transmitting the information on the speaker identification from the server to the location where the speaker's utterance was detected. The method further includes detecting speech activity locally. Extracting features, encoding the extracted features, and/or transmitting the encoded extracted features to the server, are only performed if speech activity above some predetermined threshold is detected.

Identification of a local speaker

The present invention relates to a method for speaker recognition, comprising the steps of obtaining and storing speaker information for at least one target speaker; obtaining a plurality of speech samples from a plurality of telephone calls from at least one unknown speaker; classifying the speech samples according to the at least one unknown speaker thereby providing speaker-dependent classes of speech samples; extracting speaker information for the speech samples of each of the speaker-dependent classes of speech samples; combining the extracted speaker information for each of the speaker-dependent classes of speech samples; comparing the combined extracted speaker information for each of the speaker-dependent classes of speech samples with the stored speaker information for the at least one target speaker to obtain at least one comparison result; and determining whether one of the at least one unknown speakers is identical with the at least one target speaker based on the at least one comparison result.

The invention refers to a method for comparing voice utterances, the method comprising the steps: extracting a plurality of features () from a first voice utterance of a given text sample and extracting a plurality of features () from a second voice utterance of said given text sample, wherein each feature is extracted as a function of time, and wherein each feature of the second voice utterance corresponds to a feature of the first voice utterance; applying dynamic time warping () to one or more time dependent characteristics of the first and/or second voice utterance e.g. by minimizing one or more distance measures, wherein a distance measure is a measure for the difference of a time dependent characteristic of the first voice utterance and a corresponding time dependent characteristic of the second voice utterance, and wherein a time dependent characteristic of a voice utterance is a time dependent characteristic of either a single feature or a combination of two or more features; calculating a total distance measure (), wherein the total distance measure is a measure for the difference between the first voice utterance of the given text sample and the second voice utterance of said given text sample, wherein the total distance measure is calculated based on one or more pairs of said time dependent characteristic, and wherein a pair of time dependent characteristic is calculate total composed of a time dependent characteristic of the first or second voice utterance and of a dynamically time warped () time dependent characteristic of the respectively second or first voice utterance, or wherein a pair of time dependent characteristic is composed of a dynamically time warped () time dependent characteristic of the first voice utterance and of a dynamically time warped () time dependent characteristic of the second voice utterance.

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Madrid, Spain

Marta Garcia Gomar