The article critiques the notion that a person's voice is a stable, unique biometric trace—termed a voiceprint—by reviewing historical, forensic, and technological evidence. It argues that voices are highly dynamic and context-dependent, and that the voiceprint metaphor misrepresents probabilistic speaker information as a fixed identity marker. The authors emphasize that speaker recognition should account for within-speaker variability, domain mismatch, and synthetic manipulation rather than rely on an assumed stable voiceprint.
By Tianle Yang, Cuiling Zhang, Chengzhe Sun, Siwei Lyu, Phil Rose
VIPBench is a new benchmark that measures how humans perceive voice identity, using 124,876 same-or-different judgments from 1,290 listeners on 9,800 recording pairs involving real voices, voice clones, and morphed voices of 100 public figures. It defines four tasks to evaluate how well speaker‑representation models align with human judgments, including predicting majority responses and assessing similarity across real and synthetic recordings. Eleven publicly available speech models were evaluated, showing that listeners often misidentify cloned voices and that no model reaches the noise ceiling, with the best explaining only 61% of the explainable variance.
By Xudong Tang, Matthew Groh
arXiv:2607. 21820v1 Announce Type: cross Abstract: Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks.
By Daniyal Kabir Dar, Arun Ross
The paper examines whether word error rate adequately measures privacy in speech by applying entity‑aware metrics from NLP to evaluate two obfuscation techniques. It focuses on protecting named entities and explores how fine‑tuning on entity‑rich data affects attack success across different entity categories. The authors also offer guidance on choosing metrics depending on whether the obfuscation preserves temporal alignment.
By Anjana Rajasekhar, Jule Pohlhausen, Nayana Jacob Alappattu, Anna Leschanowsky
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 introduces EvoSpeakerMem, a training‑free layer that enables online speaker‑memory construction for multi‑user spoken agents by routing speakers, updating acoustic states, and partitioning memory writes and retrievals. It also presents LoCoMo‑ID, a benchmark with hidden speaker identities, speaker‑sensitive queries, and recording‑condition shifts to evaluate such systems. Experiments show that gated updating improves identity‑gated QA while maintaining zero wrong‑user authorizations, though a fragmentation–contamination trade‑off remains.
By Yucong Zhang, Cancan Li, Bo Xiao, Ke Zeng, Ming Li