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arXiv AI By Ridwan Arefeen, Ze Li, Rong Tong, Ming Li, Xiaoxiao Miao

Exploiting Acoustic and Content-Oriented Speaker Verification Attacks Against Multilingual Voice Anonymization

Read the original on arXiv AI →

The paper investigates how acoustic and content-oriented speaker verification attacks perform against multilingual voice anonymization. It evaluates both attacker types on anonymized speech, builds a multilingual voice-converted dataset to enhance cross‑lingual generalization, and finds that attack success depends on how much linguistic utility is preserved. Acoustic‑oriented attacks generally outperform content‑oriented ones, but the performance gap narrows when linguistic information is well maintained, and the new dataset improves results while reducing cross‑lingual disparities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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