Abstract: A method of speaker authentication comprises: receiving a speech signal; dividing the speech signal into segments; and, following each segment, obtaining an authentication score based on said segment and previously received segments, wherein the authentication score represents a probability that the speech signal comes from a specific registered speaker. In response to an authentication request, an authentication result is output based on the authentication score.

Abstract: A method of speaker verification comprises receiving an audio signal representing speech. While the audio signal is being received, features of the received audio signal are extracted. The extracted features, of at least a part of the received audio signal corresponding to the speech of at least one speaker, are summarised, and the summarised extracted features are stored. In response to a request for a speaker verification process relating to at least one enrolled user, the speaker verification process is performed using the previously summarised features.

Abstract: A biometric system is tested to see whether a proposed use matches a configuration of the system. An enrolment input is received from an enrolling user, and compared with a system configuration model to obtain a configuration matching score value. The enrollment is then controlled based on a result of comparing the received enrollment input with the system configuration model. In the case of a voice biometric system, when a test input is received from a speaker, it is determined whether audio conditions applying to the test input correspond to system configuration conditions. Verification is performed by comparing the test input with a model of the speech of an enrolled user to generate a verification score for use in deciding whether to accept or reject the speaker, depending on whether it is determined that audio conditions applying to the test input correspond to the system configuration conditions.

Abstract: A method of speaker identification comprises receiving a speech signal and dividing the speech signal into segments. Following each segment, a plurality of features are extracted from a most recently received segment, and scoring information is derived from the extracted features of the most recently received segment. The scoring information derived from the extracted features of the most recently received segment is combined with previously stored scoring information derived from the extracted features of any previously received segment. The new combined scoring information is stored, and an identification score is calculated using the combined scoring information.

Abstract: A biometric system is tested to see whether a proposed use matches a configuration of the system. An enrolment input is received from an enrolling user, and compared with a system configuration model to obtain a configuration matching score value. The enrolment is then controlled based on a result of comparing the received enrolment input with the system configuration model. In the case of a voice biometric system, when a test input is received from a speaker, it is determined whether audio conditions applying to the test input correspond to system configuration conditions. Verification is performed by comparing the test input with a model of the speech of an enrolled user to generate a verification score for use in deciding whether to accept or reject the speaker, depending on whether it is determined that audio conditions applying to the test input correspond to the system configuration conditions.

Abstract: A received signal represents a user's speech. A first speaker recognition process is performed on a first portion of the received signal, to obtain a first output result. A second speaker recognition process is performed on a second portion of the received signal that is different from the first portion of the received signal, to obtain a second output result. The second speaker recognition process is different from the first speaker recognition process. The first and second output results are combined to obtain a combined output result indicating a likelihood that the user is a registered user.

Abstract: A method of speaker verification comprises receiving an audio signal representing speech. While the audio signal is being received, features of the received audio signal are extracted. The extracted features, of at least a part of the received audio signal corresponding to the speech of at least one speaker, are summarised, and the summarised extracted features are stored. In response to a request for a speaker verification process relating to at least one enrolled user, the speaker verification process is performed using the previously summarised features.

Abstract: A method for use in speaker verification, comprises: receiving a sample of a person's speech; estimating a noise measure in the received sample of the person's speech; if the estimated noise measure does not meet a predetermined criterion, rejecting the received sample of the person's speech; and if the estimated noise measure meets the predetermined criterion: adding noise to the received sample to form a sample with increased noise; extracting features of the sample with increased noise; and forming a model of the person's speech from the extracted features.

Abstract: A method for use in speaker verification, comprises: receiving a sample of a person's speech; estimating a noise measure in the received sample of the person's speech; if the estimated noise measure does not meet a predetermined criterion, rejecting the received sample of the person's speech; and if the estimated noise measure meets the predetermined criterion: adding noise to the received sample to form a sample with increased noise; extracting features of the sample with increased noise; and forming a model of the person's speech from the extracted features.

Abstract: A received signal represents a user's speech. A first speaker recognition process is performed on a first portion of the received signal, to obtain a first output result. A second speaker recognition process is performed on a second portion of the received signal that is different from the first portion of the received signal, to obtain a second output result. The second speaker recognition process is different from the first speaker recognition process. The first and second output results are combined to obtain a combined output result indicating a likelihood that the user is a registered user.

Abstract: A received signal represents a user's speech. A first speaker recognition process is performed on a first portion of the received signal, to obtain a first output result. A second speaker recognition process is performed on a second portion of the received signal that is different from the first portion of the received signal, to obtain a second output result. The second speaker recognition process is different from the first speaker recognition process. The first and second output results are combined to obtain a combined output result indicating a likelihood that the user is a registered user.

Abstract: A method of speaker authentication comprises: receiving a speech signal; dividing the speech signal into segments; and, following each segment, obtaining an authentication score based on said segment and previously received segments, wherein the authentication score represents a probability that the speech signal comes from a specific registered speaker. In response to an authentication request, an authentication result is output based on the authentication score.

Abstract: A method of speaker identification comprises receiving a speech signal and dividing the speech signal into segments. Following each segment, a plurality of features are extracted from a most recently received segment, and scoring information is derived from the extracted features of the most recently received segment. The scoring information derived from the extracted features of the most recently received segment is combined with previously stored scoring information derived from the extracted features of any previously received segment. The new combined scoring information is stored, and an identification score is calculated using the combined scoring information.

Abstract: 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.

Abstract: A method of operation of a speaker recognition system comprises: performing a speaker recognition process on a received signal; disabling the speaker recognition process when a first speaker has been identified; performing a speech start recognition process on the received signal when the speaker recognition process is disabled; and enabling the speaker recognition process in response to the speech start recognition process detecting a speech start event in the received signal.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.