Evaluating Voice Conversion-based Privacy Protection against Informed Attackers

Understand

Speech data conveys sensitive speaker attributes like identity or accent.

• With a small amount of found data, such attributes can be inferred and exploited for malicious purposes: voice cloning, spoofing, etc.
• Anonymization aims to make the data unlinkable, i.e., ensure that no utterance can be linked to its original speaker.
• In this paper, we investigate anonymization methods based on voice conversion.