Abstract: To be able to convert to a voice of the desired attribution. A learning unit learns a converter to minimize a value of a learning criterion of the converter, learns a voice identifier to minimize a value of a learning criterion of the voice identifier, and learns an attribution identifier to minimize a value of a learning criterion of the attribution identifier.

Abstract: A voice signal conversion model learning device comprising: a learning data acquisition unit that acquires learning input data which is an input voice signal; and a learning stage conversion unit that executes a conversion learning model which is a model of machine learning including learning stage conversion processing of converting the learning input data into learning stage conversion destination data which is a voice signal of a conversion destination, wherein the learning stage conversion processing includes local feature quantity acquisition processing of acquiring a feature quantity for each learning input-side subset which is a subset of processing target input data having the processing target input data as a population, based on the processing target input data which is data to be processed, the conversion learning model further includes adjustment parameter value acquisition processing of acquiring an adjustment parameter value, which is a value of a parameter for adjusting a statistical value of a

Abstract: A voice signal conversion model learning device includes: a generation unit configured to execute generation processing of generating a conversion destination voice signal on the basis of an input voice signal that is a voice signal of an input voice, conversion source attribute information that is information indicating an attribute of an input voice that is a voice represented by the input voice signal, and conversion destination attribute information indicating an attribute of a voice represented by the conversion destination voice signal that is a voice signal of a conversion destination of the input voice signal; and an identification unit configured to execute voice estimation processing of estimating whether or not a voice signal that is a processing target is a voice signal representing a vocal sound actually uttered by a person on the basis of the conversion source attribute information and the conversion destination attribute intonation, wherein the conversion destination voice signal is input to th

Abstract: A voice signal conversion model learning device including: a generation unit configured to generate a conversion destination voice signal on the basis of an input voice signal that is a voice signal of an input voice and conversion destination attribute information indicating an attribute of a voice represented by the conversion destination voice signal that is a voice signal of a conversion destination of the input voice signal; and an identification unit configured to execute a voice estimation process of estimating whether a voice signal represents a voice actually uttered by a person on the basis of the conversion destination voice signal, wherein the generation unit executes characteristic processing that is processing based on a neural network with respect to information indicating characteristics of the input voice signal and processing of converting a result of the characteristic processing based on a conversion mapping that is a mapping updated in accordance with an estimation result of the identific

Abstract: A conversion learning device includes: a source encoding unit that converts, by using a first machine learning model, a feature amount sequence of a source domain that is a characteristic of conversion-source content data, into a first internal representation vector sequence that is a matrix in which internal representation vectors at individual locations of the feature amount sequence of the source domain are arranged; a target encoding unit that converts, by using a second machine learning model, a feature amount sequence of a target domain that is a characteristic of conversion-target content data, into a second internal representation vector sequence that is a matrix in which internal representation vectors at individual locations of the feature amount sequence of the target domain are arranged; an attention matrix calculation unit that calculates, by using the first internal representation vector sequence and the second internal representation vector sequence, an attention matrix that is a matrix mapping

Abstract: A signal retrieval device includes a modification unit and a signal retrieval unit. The modification unit modifies a value of an attribute of a target represented by an input signal or a stored signals stored in a signal storage unit or a value of an attribute relating to a signal generation source of the input signal to acquire a plurality of modified values of the attribute. The signal retrieval unit retrieves a stored signal of the stored signals similar to the input signal using the input signal or the stored signals in which the attribute is modified according to each of the plurality of modified values of the attribute acquired by the modification unit.

Abstract: A voice conversion device is provided with a linguistic information extraction unit that extracts linguistic information corresponding to utterance content from a conversion source voice signal, an appearance feature extraction unit that extracts appearance features expressing features related to the look of a person's face from a captured image of the person, and a converted voice generation unit that generates a converted voice on a basis of the linguistic information and the appearance features.

Abstract: To be able to convert to a voice of the desired attribution. Learning an encoder for, on the basis of parallel data of a sound feature vector series in a conversion-source voice signal and a latent vector series in the conversion-source voice signal, and an attribution label indicating attribution of the conversion-source voice signal, estimating a latent vector series from input of a sound feature vector series and an attribution label, and a decoder for reconfiguring the sound feature vector series from input of the latent vector series and the attribution label.

Abstract: The present invention relates to methods of converting a speech into another speech that sounds more natural. The method includes learning for a target conversion function and a target identifier according to an optimal condition in which the target conversion function and the target identifier compete with each other. The target conversion function converts source speech into target speech. The target identifier identifies whether the converted target speech follows the same distribution as actual target speech. The methods include learning for a source conversion function and a source identifier according to an optimal condition in which the source conversion function and the source identifier compete with each other. The source conversion function converts target speech into source speech, and the source identifier identifies whether the converted source speech follows the same distribution as actual source speech.

Abstract: The present invention realizes conversion into data having a desired attribute. A training unit 32 trains a converter so as to minimize the value of a learning criterion for the converter, and trains an integrated discriminator so as to minimize the value of a learning criterion for the integrated discriminator.

Abstract: Accurate conversion to data of a conversion target domain is allowed. A training unit 32 trains a forward generator, an inverse generator, a conversion target discriminator, and a conversion source discriminator to optimize an objective function.

Abstract: A signal output unit outputs an acquired signal. A signal accumulation unit accumulates signals. A signal attribute value display unit displays a value of an attribute related to an element constituting a target represented by the acquired signal or a signal generation source in a state in which an instruction for changing the value of the attribute is able to be received. A changed attribute value acquisition unit acquires a changed value of the attribute when the instruction for changing the value of the attribute is received. A signal retrieval unit retrieves, from the signals accumulated in the signal accumulation unit, a signal similar to a changed signal when the value of the attribute has been changed on the basis of the changed value of the attribute acquired by the changed attribute value acquisition unit.

Abstract: To be able to convert to a voice of the desired attribution. A learning unit learns a converter to minimize a value of a learning criterion of the converter, learns a voice identifier to minimize a value of a learning criterion of the voice identifier, and learns an attribution identifier to minimize a value of a learning criterion of the attribution identifier.

Abstract: A signal output unit outputs an acquired signal. A signal attribute value display unit displays a value of an attribute related to an element constituting a target represented by the acquired signal or a signal generation source in a state in which a change instruction of the value of the attribute is able to be received. A changed attribute value acquisition unit acquires a changed value of the attribute when the change instruction of the value of the attribute is received. A change unit changes the value of the attribute for which the change instruction has been received on the basis of the changed value of the attribute acquired by the changed attribute value acquisition unit. A changed signal output unit outputs a changed signal in which the value of the attribute has been changed.

Abstract: To be able to convert to a voice of more natural audio quality.

Abstract: To be able to convert to a voice of the desired attribution. Learning an encoder for, on the basis of parallel data of a sound feature vector series in a conversion-source voice signal and a latent vector series in the conversion-source voice signal, and an attribution label indicating attribution of the conversion-source voice signal, estimating a latent vector series from input of a sound feature vector series and an attribution label, and a decoder for reconfiguring the sound feature vector series from input of the latent vector series and the attribution label.

Abstract: A signal output unit outputs an acquired signal. A signal attribute value display unit displays a value of an attribute related to an element constituting a target represented by the acquired signal or a signal generation source in a state in which a change instruction of the value of the attribute is able to be received. A changed attribute value acquisition unit acquires a changed value of the attribute when the change instruction of the value of the attribute is received. A change unit changes the value of the attribute for which the change instruction has been received on the basis of the changed value of the attribute acquired by the changed attribute value acquisition unit. A changed signal output unit outputs a changed signal in which the value of the attribute has been changed.

Abstract: A signal generation device includes a variable generation unit and a signal generation unit. The variable generation unit generates a plurality of latent variables corresponding to a plurality of features of a signal. The signal generation unit inputs, to at least one neural network learned in advance, a latent variable representing attributes obtained by converting a part of the plurality of latent variables by an attribute vector representing attributes of a signal to be generated and the other part of the plurality of latent variables representing an identity and generates the signal to be generated using the at least one neural network.

Abstract: A signal output unit outputs an acquired signal. A signal accumulation unit accumulates signals. A signal attribute value display unit displays a value of an attribute related to an element constituting a target represented by the acquired signal or a signal generation source in a state in which an instruction for changing the value of the attribute is able to be received. A changed attribute value acquisition unit acquires a changed value of the attribute when the instruction for changing the value of the attribute is received. A signal retrieval unit retrieves, from the signals accumulated in the signal accumulation unit, a signal similar to a changed signal when the value of the attribute has been changed on the basis of the changed value of the attribute acquired by the changed attribute value acquisition unit.

Abstract: A signal retrieval device includes a modification unit and a signal retrieval unit. The modification unit modifies a value of an attribute of a target represented by an input signal or a stored signals stored in a signal storage unit or a value of an attribute relating to a signal generation source of the input signal to acquire a plurality of modified values of the attribute. The signal retrieval unit retrieves a stored signal of the stored signals similar to the input signal using the input signal or the stored signals in which the attribute is modified according to each of the plurality of modified values of the attribute acquired by the modification unit.