LiveAnon: Improving Privacy and Utility in Streaming Speaker Anonymization with Codec Language Models
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 improv...
| Authors | Yejin Kim, Kyungsu Kim, Seungu Han et al. |
|---|---|
| Primary category | eess.AS |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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