This paper presents a refinement framework of WaveNet vocoders for variational autoencoder (VAE) based voice conversion (VC), which reduces the quality distortion caused by the mismatch between the training data and testing data. Conventional WaveNet vocoders are trained with natural acoustic features but condition on the converted features in the conversion stage for VC, and such mismatch often causes significant quality and similarity degradation. In this work, we take advantage of the particular structure of VAEs to refine WaveNet vocoders with the self-reconstructed features generated by VAE, which are of similar characteristics with the converted features while having the same data length with the target training data. In other words, our proposed method does not require any alignment. Objective and subjective experimental results demonstrate the effectiveness of our proposed framework.
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