Agnitio Sl

Cirrus Logic International Semiconductor Limited

Luis Buera Rodriguez

Marta Garcia Gomar

Carlos Vaquero Aviles-Casco

Carlos Vaquero Avilés-Casco

Alfonso Ortega Giménez

Johan Nikolaas Langehoven Brummer

Jesús Antonio Villalba López

Antonio Miguel Artiaga

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

System for classifying whether audio data received in a speaker recognition system is genuine or a spoof using a Gaussian classifier and method for classifying whether audio data received in a speaker recognition system is genuine or a spoof using a Gaussian classifier.

Anti-spoofing

A method for estimating the reliability of a result of a speaker recognition system concerning a testing audio and a speaker model, which is based on one, two, three or more model audios, the method using a Bayesian Network to estimate whether the result is reliable. In estimating the reliability of the result of the speaker recognition system one, two, three, four or more than four quality measures of the testing audio and one, two, three, four or more than four quality measures of the model audio(s) are used.

Estimation of reliability in speaker recognition

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

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.

Growing community of inventors

Madrid, Spain

Luis Buera Rodriguez