Continual Speaker Identity Unlearning with Minimal Interference
A new framework called CORTIS has been developed for continual speaker identity unlearning in zero-shot text-to-speech systems. This method addresses the limitations of existing techniques that assume all unlearning requests occur simultaneously. CORTIS allows for the sequential removal of speaker identities without reintroducing previously unlearned speakers, enhancing privacy protection.
- ▪CORTIS stands for Cumulative ORThogonal Identity Suppression.
- ▪The framework does not require access to previously unlearned speaker data.
- ▪CORTIS outperforms previous methods by maintaining the privacy of previously unlearned speakers during sequential requests.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2605.25962 |
| Publication time | Tue, 26 May 2026 04:10:35 +0000 |
| Retrieval time | 2026-05-26T04:37:43.007Z |
| Last seen | 2026-05-26T04:37:43.007Z |
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Computer Science > Sound arXiv:2605.25962 (cs) [Submitted on 25 May 2026] Title:Continual Speaker Identity Unlearning with Minimal Interference Authors:Jinju Kim, Yunsung Kang, Gyeong-Moon Park, Jong Hwan Ko View a PDF of the paper titled Continual Speaker Identity Unlearning with Minimal Interference, by Jinju Kim and 3 other authors View PDF HTML (experimental) Abstract:Machine unlearning removes designated concepts or knowledge from pre-trained models. Recent work has extended this paradigm to speaker identity unlearning in zero-shot text-to-speech (ZS-TTS), the task of selectively erasing a model's ability to replicate a speaker's voice.
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