Continual Speaker Identity Unlearning with Minimal Interference
📰 ArXiv cs.AI
Learn to continually unlearn speaker identities in pre-trained text-to-speech models with minimal interference, a crucial skill for privacy and data protection
Action Steps
- Implement a continual unlearning framework for speaker identity in ZS-TTS models using incremental learning techniques
- Evaluate the interference caused by sequential unlearning requests on the model's performance
- Apply regularization techniques to minimize interference and preserve model accuracy
- Test the framework on a dataset with multiple speaker identities and unlearning requests
- Compare the results with existing unlearning methods to assess the effectiveness of the proposed approach
Who Needs to Know This
AI engineers and researchers working on text-to-speech models and privacy preservation will benefit from this knowledge, as it enables them to develop more robust and adaptable models
Key Insight
💡 Continual unlearning of speaker identities is crucial for privacy preservation in text-to-speech models, and can be achieved with minimal interference using incremental learning and regularization techniques
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Continual speaker identity unlearning in ZS-TTS models with minimal interference #AI #Privacy #TextToSpeech
Key Takeaways
Learn to continually unlearn speaker identities in pre-trained text-to-speech models with minimal interference, a crucial skill for privacy and data protection
Full Article
Title: Continual Speaker Identity Unlearning with Minimal Interference
Abstract:
arXiv:2605.25962v1 Announce Type: cross 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. Existing methods, however, quietly assume all unlearning requests arrive at once; an unrealistic assumption, since privacy-motivated removals arrive sequentially over time. We
Abstract:
arXiv:2605.25962v1 Announce Type: cross 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. Existing methods, however, quietly assume all unlearning requests arrive at once; an unrealistic assumption, since privacy-motivated removals arrive sequentially over time. We
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