Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models

Nikita Kuzmin; Tao Zhong; Jiajun Deng; Yingke Zhu; Tristan Tsoi; Tianxiang Cao; Simon Lui; Kong Aik Lee; Eng Siong Chng

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arXiv · 2603.08179

Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models

Nikita Kuzmin·Tao Zhong·Jiajun Deng·Yingke Zhu·Tristan Tsoi·Tianxiang Cao·Simon Lui·Kong Aik Lee·Eng Siong Chng

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Abstract

End-to-end full-duplex speech models feed user audio through an always-on LLM backbone, yet the speaker privacy implications of their hidden representations remain unexamined. Following the VoicePrivacy 2024 protocol with a lazy-informed attacker, we show that the hidden states of SALM-Duplex and Moshi leak substantial speaker identity across all transformer layers. Layer-wise and turn-wise analyses reveal that leakage persists across all layers, with SALM-Duplex showing stronger leakage in early layers while Moshi leaks uniformly, and that Linkability rises sharply within the first few turns. We propose two streaming anonymization setups using Stream-Voice-Anon: a waveform-level front-end (Anon-W2W) and a feature-domain replacement (Anon-W2F). Anon-W2F raises EER by over 3.5x relative to the discrete encoder baseline (11.2% to 41.0%), approaching the 50% random-chance ceiling, while Anon-W2W retains 78-93% of baseline sBERT across setups with sub-second response latency (FRL under 0.8 s).

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Nikita Kuzmin, Tao Zhong, Jiajun Deng, Yingke Zhu, Tristan Tsoi, Tianxiang Cao, Simon Lui, Kong Aik Lee, Eng Siong Chng. 2026-03-09. Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models. https://arxiv.org/abs/2603.08179

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