DisC-VC: Disentangled and F0-Controllable Neural Voice Conversion

Voice conversion is a task to convert a non-linguistic feature of a given utterance. Since nuance of speech strongly depends on its pitch pattern, in some applications, it would be desirable to keep the original rise/fall pitch pattern while changing the speaker identity. Some of the existing methods address this problem by either using a source-filter model or developing a neural network that takes an F
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pattern as input to the model. Although the latter approach can achieve relatively high sound quality compared to the former one, there is no consideration for discrepancy between the target and generated F
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patterns in its training process. In this paper, we propose a new variational-autoencoder-based voice conversion model accompanied by an auxiliary network, which ensures that the conversion result correctly reflects the specified F
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/timbre information. We show the effectiveness of the proposed method by objective and subjective evaluations.