关键词: 语音转换, 语音个性特征, 深度信念网络模型, 高阶空间

Abstract: This paper presents a voice conversion technique using Deep Belief Nets（DBN） to build high-order eigen spaces of the source/target speakers, where it is easier to convert the source speech to the target speech than in the traditional cepstrum space. Training the DBNs for a source speaker and a target speaker, it can then connect and convert the speaker individuality abstractions using Artificial Neural Networks（ANN）. The converted abstraction of the source speaker is then brought back to the cepstrum space using an inverse process of the DBNs of the target speaker. It conducts speaker voice conversion experiments and confirms the efficacy of the method with respect to subjective and objective criteria, when comparing it with the conventional Gaussian Mixture Model-based method.

Key words: voice conversion, speaker characteristics, deep belief networks, high-order eigen spaces