Voice conversion method, electronic device, and storage medium

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected to the at least one processor;
• wherein,
• the memory is configured to store instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is caused to perform:
• acquiring a source speech of a first user and a reference speech of a second user;
• extracting first speech content information and a first acoustic feature from the source speech;
• extracting a second acoustic feature from the reference speech;
• acquiring a reconstructed third acoustic feature by inputting the first speech content information, the first acoustic feature, and the second acoustic feature into a pre-trained voice conversion model, in which the pre-trained voice conversion model is acquired by training based on speeches of a third user;
• and
• synthesizing a target speech based on the third acoustic feature.

A non-transitory computer-readable storage medium having stored therein instructions that, when executed by a computer, the computer is caused to implement a voice conversion method comprising:

• acquiring a source speech of a first user and a reference speech of a second user;
• extracting first speech content information and a first acoustic feature from the source speech;
• extracting a second acoustic feature from the reference speech;
• acquiring a reconstructed third acoustic feature by inputting the first speech content information, the first acoustic feature, and the second acoustic feature into a pre-trained voice conversion model, in which the pre-trained voice conversion model is acquired by training based on speeches of a third user;
• and
• synthesizing a target speech based on the third acoustic feature.

The non-transitory computer-readable storage medium as claimed in 17, wherein the voice conversion model comprises a hidden-variable network, a timbre network, and a reconstruction network, and acquiring the reconstructed third acoustic feature by inputting the first speech content information, the first acoustic feature, and the second acoustic feature into the pre-trained voice conversion model comprises:

• acquiring a fundamental frequency and an energy parameter by inputting the first acoustic feature into the hidden-variable network;
• acquiring a timbre parameter by inputting the second acoustic feature into the timbre network;
• and
• acquiring the third acoustic feature by inputting the first speech content information, the fundamental frequency and energy parameter, and the timbre parameter into the reconstruction network.

The non-transitory computer-readable storage medium as claimed in 17, wherein the method further comprises:

• acquiring a first speech and a second speech of the third user;
• extracting second speech content information and a fourth acoustic feature from the first speech;
• extracting a fifth acoustic feature from the second speech;
• acquiring a reconstructed sixth acoustic feature by inputting the second speech content information, the fourth acoustic feature, and the fifth acoustic feature into a voice conversion model to be trained;
• adjusting model parameters in the voice conversion model to be trained based on a difference between the sixth acoustic feature and the fourth acoustic feature, and returning to the acquiring the first speech and the second speech of the third user until the difference between the sixth acoustic feature and the fourth acoustic feature satisfies a preset training end condition;
• and
• determining the voice conversion model to be trained after a last adjusting of model parameters as the pre-trained voice conversion model.