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:2605.16578v4 Announce Type: replace-cross
Abstract: Artificially generated speech is increasingly embedded in everyday life. Voice cloning in particular enables applications where identity pres...
By Kaitlyn Zhou, Federico Bianchi, Martijn Bartelds, Anna Pot, Yongchan Kwon, James Zou
The paper "Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identification" argues that evaluating speaker de-identification systems solely by Equal Error Rate (EER) is insufficient. It proposes a holistic framework using five complementary metrics—EER, soft biometric leakage score, cumulative match characteristic re-identification analysis, canonical correlation analysis with Procrustes embedding alignment, and intelligibility via word error rate and semantic similarity—to capture independent dimensions of information leakage. Experiments on five IARPA ARTS SDID systems show that these metrics reveal leakage that a single metric would miss.
By Seungmin Seo, Oleg Aulov, P. Jonathon Phillips, Kevin Mangold, Jonathan Eskin
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
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
arXiv:2609.39344v1 Announce Type: cross
Abstract: Speech transcripts used as long-term memory must preserve both words and stable speaker identities. Existing meeting-transcription metrics either ign...
By Shantanu Vispute, Aditya Mishra, Siddhartha Saxena