An electronic device is disclosed. The electronic device comprises: a voice input unit; a storage unit for storing a first text according to a first transcript format and at least one second text obtained by transcribing the first text in a second transcript format; and a processor for, when a voice text converted from a user voice input through the voice input unit corresponds to a preset instruction, executing a function according to the preset instruction. The processor executes a function according to a preset instruction when the preset instruction includes a first text and a voice text is a text in which the first text of the preset instruction has been transcribed into a second text of a second transcript format.

An electronic device is disclosed. The electronic device comprises: a voice input unit; a storage unit for storing a first text according to a first transcript format and at least one second text obtained by transcribing the first text in a second transcript format; and a processor for, when a voice text converted from a user voice input through the voice input unit corresponds to a preset instruction, executing a function according to the preset instruction. The processor executes a function according to a preset instruction when the preset instruction includes a first text and a voice text is a text in which the first text of the preset instruction has been transcribed into a second text of a second transcript format.

A soundalike system to improve speech synthesis by training a text to speech engine on a voice like the target speakers voice

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.

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.

Techniques are provided for reverberation compensation for far-field speaker recognition. A methodology implementing the techniques according to an embodiment includes receiving an authentication audio signal associated with speech of a user and extracting features from the authentication audio signal. The method also includes scoring results of application of one or more speaker models to the extracted features. Each of the speaker models is trained based on a training audio signal processed by a reverberation simulator to simulate selected far-field environmental effects to be associated with that speaker model. The method further includes selecting one of the speaker models, based on the score, and mapping the selected speaker model to a known speaker identification or label that is associated with the user.

Techniques are provided for reverberation compensation for far-field speaker recognition. A methodology implementing the techniques according to an embodiment includes receiving an authentication audio signal associated with speech of a user and extracting features from the authentication audio signal. The method also includes scoring results of application of one or more speaker models to the extracted features. Each of the speaker models is trained based on a training audio signal processed by a reverberation simulator to simulate selected far-field environmental effects to be associated with that speaker model. The method further includes selecting one of the speaker models, based on the score, and mapping the selected speaker model to a known speaker identification or label that is associated with the user.

In one aspect, a method includes receiving a prompt for use with text-dependent speaker verification; generating a linguistic representation of the prompt, wherein the linguistic representation comprises a sequence of speech units; obtaining a plurality of feature vectors or a plurality of acoustic models; generating a universal background model for the prompt using the plurality of feature vectors or the plurality of acoustic models; receiving audio enrollment data of a first speaker speaking the prompt; and creating a first speaker verification model for the first speaker by adapting the universal background model using the audio enrollment data.

In one aspect, a method includes receiving a prompt for use with text-dependent speaker verification; generating a linguistic representation of the prompt, wherein the linguistic representation comprises a sequence of speech units; obtaining a plurality of feature vectors or a plurality of acoustic models; generating a universal background model for the prompt using the plurality of feature vectors or the plurality of acoustic models; receiving audio enrollment data of a first speaker speaking the prompt; and creating a first speaker verification model for the first speaker by adapting the universal background model using the audio enrollment data.

A security platform architecture is described herein. A user identity platform architecture which uses a multitude of biometric analytics to create an identity token unique to an individual human. This token is derived on biometric factors like human behaviors, motion analytics, human physical characteristics like facial patterns, voice recognition prints, usage of device patterns, user location actions and other human behaviors which can derive a token or be used as a dynamic password identifying the unique individual with high calculated confidence. Because of the dynamic nature and the many different factors, this method is extremely difficult to spoof or hack by malicious actors or malware software.