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We introduce DISSC, a novel, lightweight method that converts the rhythm,pitch contour and timbre of a recording to a target speaker in a textlessmanner. Unlike DISSC, most voice conversion (VC) methods focus primarily ontimbre, and ignore people's unique speaking style (prosody). The proposedapproach uses a pretrained, self-supervised model for encoding speech todiscrete units, which makes it simple, effective, and fast to train. Allconversion modules are only trained on reconstruction like tasks, thus suitablefor any-to-many VC with no paired data. We introduce a suite of quantitativeand qualitative evaluation metrics for this setup, and empirically demonstratethat DISSC significantly outperforms the evaluated baselines. Code and samplesare available at https://pages.cs.huji.ac.il/adiyoss-lab/dissc/.

Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Units