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One-shot style transfer is a challenging task, since training on oneutterance makes model extremely easy to over-fit to training data and causeslow speaker similarity and lack of expressiveness. In this paper, we build onthe recognition-synthesis framework and propose a one-shot voice conversionapproach for style transfer based on speaker adaptation. First, a speakernormalization module is adopted to remove speaker-related information inbottleneck features extracted by ASR. Second, we adopt weight regularization inthe adaptation process to prevent over-fitting caused by using only oneutterance from target speaker as training data. Finally, to comprehensivelydecouple the speech factors, i.e., content, speaker, style, and transfer sourcestyle to the target, a prosody module is used to extract prosodyrepresentation. Experiments show that our approach is superior to thestate-of-the-art one-shot VC systems in terms of style and speaker similarity;additionally, our approach also maintains good speech quality.

One-shot Voice Conversion For Style Transfer Based On Speaker Adaptation