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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
Anonymization, Not Elimination: Utility-Preserved Speech Anonymization
TL;DR
A novel two-stage framework for speech anonymization preserves both linguistic content and acoustic identity, achieving stronger privacy protection with minimal utility loss. This approach enhances the usability of anonymized speech for tasks like Automatic Speech Recognition and Speech Emotion Recognition.
✦ Why It Matters
Implement this framework to enhance privacy in speech data without sacrificing performance in downstream applications.
Key Takeaways
- Adopt the two-stage framework for effective speech anonymization.
- Utilize generative models to replace personally identifiable information.
- Evaluate anonymized speech using comprehensive privacy and utility metrics.
- Leverage the F3-VA framework for diverse speaker anonymization.
How It Works
The framework operates in two stages: first, it uses a generative speech editing model to replace PII, ensuring linguistic content remains intact. Second, it employs F3-VA, a flow-matching-based approach that generates distinct anonymized voices, enhancing voice privacy while preserving the original speech's utility.