The paper introduces a two‑stage speech anonymization framework that preserves both linguistic content and acoustic identity. It replaces personally identifiable information using a generative editing model and applies a flow‑matching anonymization technique (F3‑VA) to create diverse, distinct anonymized speakers. The authors evaluate privacy with speaker verification metrics and utility by training ASR, TTS, and SER models from scratch, showing stronger privacy protection with minimal utility loss compared to existing baselines.
By Yunchong Xiao, Yuxiang Zhao, Ziyang Ma, Shuai Wang, Kai Yu, Jiachun Liao, Xie Chen
The paper demonstrates that the multilingual voice cloning model XTTSv2 can be repurposed for speaker anonymization without retraining. By conditioning on a pseudo-speaker and using an iterative refinement strategy, the authors balance privacy and intelligibility, achieving near‑optimal privacy (EER ≈ 0.49) and competitive speech quality across seven European languages. The method outperforms dedicated anonymization baselines and requires no language‑specific training.
By Romolo Muletta, Felix Matthias Saaro, Mark Cieliebak, Jan Deriu
arXiv:2607. 09767v1 Announce Type: cross Abstract: The paper presents a voice anonymization model focusing on preserving content rather than producing realistic speech.
By Adrien Schneider (M-PSI), Kacper Zabkowski (M-PSI), Anderson Augusma (M-PSI), Fr\'ed\'erique Letu\'e (SAM, SVH), Maria Camila Pinzon (M-PSI), Dominique Vaufreydaz (M-PSI)
arXiv:2607. 03985v1 Announce Type: cross Abstract: Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Speaker Anonymization Systems (SAS).
By Meiying Melissa Chen, Anastasia Kuznetsova, Zhenyu Wang, Zhiyao Duan
DiffAnon is a diffusion‑based voice anonymization method that uses classifier‑free guidance to give users continuous, inference‑time control over how much prosody is preserved. By refining acoustic detail over semantic embeddings from an RVQ codec, the model allows smooth interpolation between strong anonymization and high prosodic fidelity within a single architecture. This is the first framework to provide structured, interpolatable prosody control while maintaining competitive privacy and utility across different operating points.
By Ismail Rasim Ulgen, Zexin Cai, Nicholas Andrews, Philipp Koehn, Berrak Sisman
arXiv:2509. 15001v3 Announce Type: replace-cross Abstract: Child-centered daylong recordings are essential for studying early language development, but existing speech models trained on clean adult data perform poorly due to acoustic and linguistic differences.
By Th\'eo Charlot, Tarek Kunze, Maxime Poli, Alejandrina Cristia, Emmanuel Dupoux, Marvin Lavechin
The paper introduces VoxPrivacy, a benchmark for assessing interactional privacy in Speech Language Models (SLMs). It evaluates models on a 32‑hour bilingual dataset across three difficulty tiers, revealing that most open‑source SLMs perform near random on conditional privacy decisions and even strong closed‑source systems struggle with proactive privacy inference. The authors also validate these findings on a real‑speech subset and show that fine‑tuning on a 4,000‑hour training set can improve privacy‑preserving capabilities while maintaining robustness.
By Yuxiang Wang, Hongyu Liu, Dekun Chen, Xueyao Zhang, Zhizheng Wu
arXiv:2607. 03201v1 Announce Type: cross Abstract: Long-form recordings (LFRs) of child-centered audio are ecologically valid sources for studying early language development, but three problems limit their use.
By Kaveri K. Sheth, Lawrence Borst, Tarek Kunze, Marvin Lavechin, Okko R\"as\"anen, Sho Tsuji, Loann Peurey, Alix Bourr\'ee, Alejandrina Cristia
PHONOS is a real‑time streaming module for speaker anonymization that neutralizes accent cues by converting non‑native segmental realizations toward a target accent domain. It uses pre‑generated golden utterances that preserve timbre and rhythm, aligning them with silence‑aware DTW and applying zero‑shot voice conversion to supervise a causal accent translator. The system achieves an 81% reduction in non‑native accent confidence, improves accentedness ratings, reduces speaker linkability in embedding space, and operates with ≤241 ms end‑to‑end latency on a single GPU.
By Waris Quamer, Mu-Ruei Tseng, Ghady Nasrallah, Ricardo Gutierrez-Osuna
arXiv:2609.13195v1 Announce Type: new
Abstract: Large Audio-Language Models (LALMs) have recently shown strong capabilities in speech understanding and question answering (QA), but they also inherit...
By Zhe Liu
arXiv:2606. 01134v1 Announce Type: cross Abstract: Automatically distinguishing child-directed speech from adult-directed speech in long-form recordings is key to scalable analyses of children's language environments.
By Th\'eo Charlot, Tarek Kunze, Kaveri K. Sheth, Alejandrina Cristia, Marvin Lavechin
SpeechLLMs used in professional settings often undergo domain customisation through prompts or fine‑tuning, which can inadvertently cause the model to transcribe phonetically similar words from its context or training data, leaking private information. The authors systematically investigate this overlooked privacy risk, creating benchmarks to measure leakage rates for both prompting and fine‑tuning, and find that both mechanisms cause measurable leakage that compounds when combined. They evaluate a prompt‑level mitigation strategy and analyse the accuracy‑leakage trade‑off, concluding that fine‑tuning without context prompts offers the best balance between performance and privacy.
By Maike Z\"ufle, Jan Niehues