Audio signal conversion model learning apparatus, audio signal conversion apparatus, audio signal conversion model learning method and program

A voice signal conversion device comprising:

• a processor;
• and
• a storage medium having computer program instructions stored thereon, wherein the computer program instruction, when executed by the processor, perform processing of:
• acquiring a voice signal of a conversion target;
• and
• performing conversion of the conversion target by using a learned conversion learning model obtained by a voice signal conversion model learning device comprising:
• a processor;
• and a storage medium having computer program instructions stored thereon, wherein the computer program instruction, when executed by the processor, perform processing of:
• acquiring input data for learning, the input data being a voice signal input;
• executing a conversion learning model that is a model of machine learning that converts the input data for learning into learning stage conversion destination data that is a voice signal of a conversion destination;
• and updating the conversion learning model by learning, wherein a probability density function is defined as a target feature amount distribution function, the probability density function being a function on a vector space representing a series of voice feature amounts that are feature amounts obtained from a voice signal and representing a distribution of a series of voice feature amounts of a target voice signal that is a voice signal having a predetermined attribute, a point is defined as an initial value point, the point being in the vector space and representing a series of feature amounts of the input data for learning, a function is defined as a score function, the function having a point x in the vector space as an independent variable and indicating a gradient of a path from the point x to a nearest stationary point that is a stationary point on the target feature amount distribution function and is a stationary point nearest to the initial value point, the input data for learning is converted into the learning stage conversion destination data on a basis of the score function in the executing, and the score function in updating the conversion learning model in the updating.

A voice signal conversion model learning method comprising:

• acquiring input data for learning, the input data being a voice signal input;
• executing a conversion learning model that is a model of machine learning that converts the input data for learning into learning stage conversion destination data that is a voice signal of a conversion destination;
• and
• updating the conversion learning model by learning,
• wherein
• a probability density function is defined as a target feature amount distribution function, the probability density function being a function on a vector space representing a series of voice feature amounts that are feature amounts obtained from a voice signal and representing a distribution of a series of voice feature amounts of a target voice signal that is a voice signal having a predetermined attribute,
• a point is defined as an initial value point, the point being in the vector space and representing a series of feature amounts of the input data for learning,
• a function is defined as a score function, the function having a point x in the vector space as an independent variable and indicating a gradient of a path from the point x to a nearest stationary point that is a stationary point on the target feature amount distribution function and is a stationary point nearest to the initial value point,
• in the executing, the input data for learning is converted into the learning stage conversion destination data on a basis of the score function, and
• in the updating, the score function is updated in updating the conversion learning model.