Papers › Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Units
Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Units
Gallil Maimon, Yossi Adi
We introduce DISSC, a novel, lightweight method that converts the rhythm, pitch contour and timbre of a recording to a target speaker in a textless manner. Unlike DISSC, most voice conversion (VC) methods focus primarily on timbre, and ignore people's unique speaking style (prosody). The proposed approach uses a pretrained, self-supervised model for encoding speech to discrete units, which makes it simple, effective, and fast to train. All conversion modules are only trained on reconstruction like tasks, thus suitable for any-to-many VC with no paired data. We introduce a suite of quantitative and qualitative evaluation metrics for this setup, and empirically demonstrate that DISSC significantly outperforms the evaluated baselines. Code and samples are available at https://pages.cs.huji.ac.il/adiyoss-lab/dissc/.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Voice Conversion | VCTK | DISSC | Phone Length Error (PLE) | 0.023 | #1 of 1 | Archive leaderboard | report |
| Voice Conversion | VCTK | DISSC | Total Length Error (TLE) | 0.832 | #1 of 1 | Archive leaderboard | report |
| Voice Conversion | VCTK | DISSC | Word Length Error (WLE) | 0.056 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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