LiveAnon: Improving Privacy and Utility in Streaming Speaker Anonymization with Codec Language Models

Abstract:Speaker anonymization (SA) aims to conceal speaker identity while preserving linguistic content and emotion, but existing streaming systems often lose emotion information through discrete content bottlenecks or require emotion-specific modeling. We present LiveAnon, a voice-conversion-based streaming SA system that directly conditions an autoregressive codec language model on hidden features from a pretrained streaming ASR encoder, preserving emotion without emotion-specific supervision. To reduce codec-generation latency, we introduce capped RVQ decoding, which preserves dependencies among coarse codebooks while predicting the remaining codebooks in parallel. Under VPC24, LiveAnon improves intelligibility and emotion preservation over fixed-target Stream-Voice-Anon (SVA), a strong prior streaming SA system, matches emotion-prompted SVA in emotion preservation, and achieves the strongest semi-informed privacy among streaming systems. Under VPC26, it ranks second across all metrics among the evaluated anonymization systems, demonstrating a strong privacy-utility balance.

Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)

arXiv:2610.15379 [eess.AS]

(or arXiv:2610.15379v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2610.15379 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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