Abstract: A method for training a voice conversion model includes acquiring a voice sample set that includes training voice samples corresponding to training object identifiers, a training voice sample corresponding to a piece of voice content, and at least one training voice sample being a synthesized voice sample; extracting voice features of the training voice samples to obtain initial voice features; fusing initial voice features that belong to different training object identifiers and have corresponding voice content matching each other to obtain target voice features; obtaining a voice feature set; and inputting a voice feature in the voice feature set into an initial voice conversion model to obtain predicted voice data, and adjusting a model parameter of the initial voice conversion model based on a difference between the predicted voice data and target voice data until a first convergence condition is met, to obtain a target voice conversion model.