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Voice conversion is widely used in education? entertainment? medical and other fields. In order to obtain high-quality converted speech, this paper proposes a voice conversion algorithm based on multi-spectral feature generative adversarial network. It uses generative adversarial network to convert the voiceprint obtained by spectral feature parameters. The feature-level multimodal fusion technique is used to make the network learn multiple spectral feature information from different feature domains? so as to improve the perception of speech signals of the network. Finally, the high-quality converted speech with good definition and intelligibility is obtained. The experimental results show that the proposed algorithm is significantly superior to the traditional algorithms in the subjective and objective evaluation indicators. [ABSTRACT FROM AUTHOR]