Patent ID: 11869486
Assignee: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. A voice conversion learning method comprising:
learning, by a learner, on the basis of a sound feature value series for each of conversion-source voice signals with different attributions, and attribution codes indicating each attribution of the conversion-source voice signals, a converter configured to convert, for input of a sound feature value series and an attribution code, to a sound feature value series of a voice signal of an attribution indicated by the attribution code, learning the converter to minimize a value of a learning criterion represented using:
real voice similarity of a sound feature value series converted by the converter for input of any attribution code, the real voice similarity being associated with the any attribution code, the real voice similarity being identified a voice identifier for identifying, for input of an attribution code, whether a voice is a real voice with an attribution indicated by the attribution code or a synthetic voice,
attribution code similarity of a sound feature value series converted by the converter for input of any attribution code, the attribution code similarity being similarity to the any attribution code identified by an attribution identifier,
an error between a sound feature value series reconverted from the sound feature value series converted by the converter for input of an attribution code different from the attribution code of the conversion-source voice signal, the reconversion being done by the converter for input of the attribution code of the conversion-source voice signal, and

the sound feature value series of the conversion-source voice signal, and
a distance between the sound feature value series converted by the converter for input of the attribution code of the conversion-source voice signal and the sound feature value series of the conversion-source voice signal;

learning the voice identifier to minimize a value of a learning criterion represented using:
real voice similarity of a sound feature value series converted by the converter for input of any attribution code, the real voice similarity being associated with the any attribution code, the real voice similarity being identified by the voice identifier for identifying, for input of an attribution code, whether a voice is a real voice with an attribution indicated by the attribution code or a synthetic voice, and
real voice similarity indicated by the attribution code of the sound feature value series of the conversion-source voice signal, the real voice similarity being identified by the voice identifier for input of the attribution code of the conversion-source voice signal;

learning the attribution identifier to minimize a value of a learning criterion represented using attribution code similarity of the sound feature value series of the conversion-source voice signal, the attribution code similarity being of the conversion-source voice signal identified by the attribution identifier; and
estimating, by a voice converter, a sound feature value series of a target voice signal from a sound feature value series in an input conversion-source voice signal and the attribution code indicating an attribution of the target voice signal, using the converter for converting, for input of the sound feature value series and the attribution code, to the sound feature value series of the voice signal of an attribution indicated by the attribution code, the converter being previously learned to minimize, on the basis of the sound feature value series for each of conversion-source voice signals with different attributions, and attribution codes indicating each attribution of the conversion-source voice signals, value of the learning criterion represented using:
real voice similarity of the sound feature value series converted by the converter for input of any attribution code, the real voice similarity being associated with the any attribution code, the real voice similarity being identified the voice identifier for identifying, for input of an attribution code, whether the voice is the real voice with an attribution indicated by the attribution code or the synthetic voice,
attribution code similarity of the sound feature value series converted by the converter for input of any attribution code, the attribution code similarity being similarity to the any attribution code identified by an attribution identifier,
an error between a sound feature value series reconverted from the sound feature value series converted by the converter for input of the attribution code different from the attribution code of the conversion-source voice signal, the reconversion being done by the converter for input of the attribution code of the conversion-source voice signal, and the sound feature value series of the conversion-source voice signal, and
a distance between the sound feature value series converted by the converter for input of the attribution code of the conversion-source voice signal and the sound feature value series of the conversion-source voice signal,

the voice identifier being previously learned to minimize the value of a learning criterion represented using:
real voice similarity of the sound feature value series converted by the converter for input of any attribution code, the real voice similarity being associated with the any attribution code, the real voice similarity being identified by the voice identifier for identifying, for input of the attribution code, whether the voice is the real voice with an attribution indicated by the attribution code or the synthetic voice, and
real voice similarity indicated by the attribution code of the sound feature value series of the conversion-source voice signal, the real voice similarity being identified by the voice identifier for input of the attribution code of the conversion-source voice signal, and

the attribution identifier being previously learned to minimize value of the learning criterion represented using attribution code similarity of the sound feature value series of the conversion-source voice signal, the attribution code similarity being of the conversion-source voice signal identified by the attribution identifier.