| cff-version: 1.2.0 | |
| message: "If you use VIPBench in your research, please cite the accompanying NeurIPS 2026 paper." | |
| title: "VIPBench: A Human-Aligned Benchmark for Voice Identity Perception in the Age of Voice Cloning" | |
| abstract: "VIPBench is a benchmark of 124,876 same/different identity judgments from 1,290 English-speaking listeners on 9,800 voice pairs across 100 speakers, spanning real recordings, AI voice clones, and continuously morphed voices. The release includes audio, listener judgments, and pre-extracted embeddings for ten speaker and speech-representation models. Four evaluation tasks measure model alignment with human voice-identity perception." | |
| type: dataset | |
| authors: | |
| - family-names: "Anonymous" | |
| given-names: "Authors" | |
| affiliation: "Withheld for double-blind review" | |
| license: CC-BY-NC-4.0 | |
| version: "1.0" | |
| date-released: "2026-05-06" | |
| keywords: | |
| - speaker embeddings | |
| - voice identity perception | |
| - human-aligned benchmark | |
| - voice cloning | |
| - perceptual evaluation | |
| preferred-citation: | |
| type: conference-paper | |
| title: "VIPBench: A Human-Aligned Benchmark for Voice Identity Perception in the Age of Voice Cloning" | |
| authors: | |
| - family-names: "Anonymous" | |
| given-names: "Authors" | |
| collection-title: "Advances in Neural Information Processing Systems Datasets and Benchmarks (NeurIPS Evaluations and Datasets Track)" | |
| year: 2026 | |
| notes: "Anonymized for double-blind review. Author identities and DOI to be added at camera-ready." | |