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Prosody modeling is important, but still challenging in expressive voiceconversion. As prosody is difficult to model, and other factors, e.g., speaker,environment and content, which are entangled with prosody in speech, should beremoved in prosody modeling. In this paper, we present IQDubbing to solve thisproblem for expressive voice conversion. To model prosody, we leverage therecent advances in discrete self-supervised speech representation (DSSR).Specifically, prosody vector is first extracted from pre-trained VQ-Wav2Vecmodel, where rich prosody information is embedded while most speaker andenvironment information are removed effectively by quantization. To furtherfilter out the redundant information except prosody, such as content andpartial speaker information, we propose two kinds of prosody filters to sampleprosody from the prosody vector. Experiments show that IQDubbing is superior tobaseline and comparison systems in terms of speech quality while maintainingprosody consistency and speaker similarity.

IQDUBBING: Prosody modeling based on discrete self-supervised speech representation for expressive voice conversion