PHONOS: PHOnetic Neutralization for Online Streaming Applications
For speaker anonymization systems, PHONOS reduces the risk of speaker identification via accent, a previously unaddressed bottleneck.
PHONOS addresses the problem of accent leakage in speaker anonymization by neutralizing non-native accents in real-time, achieving an 81% reduction in non-native accent confidence and latency under 241 ms on a single GPU.
Speaker anonymization (SA) systems modify timbre while leaving regional or non-native accents intact, which is problematic because accents can narrow the anonymity set. To address this issue, we present PHONOS, a streaming module for real-time SA that neutralizes non-native accent to sound native-like. Our approach pre-generates golden speaker utterances that preserve source timbre and rhythm but replace foreign segmentals with native ones using silence-aware DTW alignment and zero-shot voice conversion. These utterances supervise a causal accent translator that maps non-native content tokens to native equivalents with at most 40ms look-ahead, trained using joint cross-entropy and CTC losses. Our evaluations show an 81% reduction in non-native accent confidence, with listening-test ratings consistent with this shift, and reduced speaker linkability as accent-neutralized utterances move away from the original speaker in embedding space while having latency under 241 ms on single GPU.