--- license: cc-by-nc-4.0 language: - en pretty_name: VIPBench size_categories: - 100K **Anonymized release for NeurIPS 2026 Evaluations & Datasets Track double-blind review.** > Author identities and permanent URLs will be added at camera-ready. --- ## Dataset summary | Item | Count | |---|---| | Speakers | 100 (50 M / 50 F, 5 sociophonetic groups, 2 age brackets) | | Reference audio clips | 100 (one per speaker) | | Comparison audio clips | 9,800 (98 per speaker) | | Voice pairs | 9,800 | | Listener judgments | 124,876 | | Listeners | 1,290 | | Median judgments per pair | 10 (range 8 to 92) | | Stimulus types | 6 (real same/different, AI clones, voice morphs) | | Pre-extracted speaker embeddings | 10 models | | Per-layer SSL embeddings | 5 models | ## Supported tasks The benchmark defines four evaluation tasks: 1. **Predict listener agreement rate** (continuous regression). Predict `P(same)` per pair. Metric: Pearson r, R^2 against the human consensus, bounded by the Spearman-Brown noise ceiling rho_SB = 0.705. 2. **Human-aligned binary verification**. Classify pairs against the human majority vote. Metrics: AUC (ranking) and Platt-calibrated ECE (calibration). 3. **Representational similarity (RSA)**. Spearman correlation between human and model representational dissimilarity matrices, with a Mantel permutation test. 4. **Real-to-synthetic transfer**. Whether a predictor fit on real-speech pairs still works on voice clones and morphs. A 10-fold gender-balanced speaker-level cross-validation protocol prevents speaker leakage. ## Dataset structure ``` data/ speakers.csv # 100 rows: speaker id, name, group, gender, age stimuli.csv # 9,800 rows: per-pair aggregates (P(same), votes, type) participant_responses.csv # 124,876 rows: per-judgment records stimuli_interpol.csv # 8,100 rows: morph-trajectory metadata for Type 6 audio/ reference/ # 100 *R.wav (16 kHz mono) comparison/ # 9,800 *.wav embeddings/ rawnet3.npz, ecapa_tdnn.npz, titanet.npz, xvector.npz, resemblyzer.npz, wav2vec2.npz, hubert.npz, wavlm.npz, xlsr.npz, whisper.npz layers/ # per-layer (mean-pooled) for SSL models wav2vec2.npz, hubert.npz, wavlm.npz, xlsr.npz, whisper.npz samples/ # 5-speaker quick-look subset (~150 MB) code/ # 10 extraction scripts + analysis notebook + reproduce.sh docs/ # annotation protocol, schemas, model table, reproduction ``` For column-level dictionaries see `docs/data_dictionary.md`. For the six stimulus types see `docs/stimulus_types.md`. For the listening-study protocol see `docs/annotation_protocol.md`. ### Embedding format Each `.npz` is a key-value store keyed by audio basename without the `.wav` extension (e.g., `M01R`, `1_F01`, `4_M12_M15B`). Values are numpy arrays of shape `(embedding_dim,)` for the 10 main embeddings and `(num_layers, embedding_dim)` for the per-layer bundles. The 9,900 keys cover 100 references plus 9,800 comparisons. ### Pairing reference and comparison Each row of `data/stimuli.csv` represents one voice pair. The `reference` column gives the reference speaker ID (e.g., `M01`) and the `id` column gives the stimulus identifier of the comparison clip (e.g., `1_M01`, `4_M12_M15B`). The pairing rule is: | You want | Reference clip | Comparison clip | |---|---|---| | Audio file | `data/audio/reference/{row.reference}R.wav` | `data/audio/comparison/{row.id}.wav` | | Embedding key | `{row.reference}R` (e.g., `M01R`) | `{row.id}` (e.g., `1_M01`) | Cosine-similarity scoring against `P(same)`: ```python import numpy as np, pandas as pd from sklearn.metrics.pairwise import cosine_similarity stim = pd.read_csv('data/stimuli.csv') emb = dict(np.load('data/embeddings/ecapa_tdnn.npz')) stim['cos'] = stim.apply( lambda r: cosine_similarity( emb[f'{r.reference}R'].reshape(1, -1), emb[r.id].reshape(1, -1) )[0, 0], axis=1, ) stim['p_same'] = stim['same_vote'] / stim['num_response'] print(stim[['cos', 'p_same']].corr()) # Pearson r against listener consensus ``` ## Quick start ```bash pip install -r requirements.txt cd code && bash reproduce.sh # ~10 min from cached embeddings ``` To re-extract embeddings from the audio (~24 CPU-hours plus ~1 GPU-hour for Whisper), see `docs/reproduction.md`. ## Source data and collection - **Speakers.** 100 English-speaking US celebrities stratified across 5 sociophonetic groups (1 = New York City English, 2 = Southern American English, 3 = African American English, 4 = Latino English, 5 = Asian American English) x 2 genders x 2 age brackets (1 = under 45, 2 = 55 or older), 5 speakers per cell. - **Reference audio.** Clips selected from publicly available recordings (interviews, podcasts). - **Voice clones.** Generated with Cartesia (a state-of-the-art TTS system) seeded from a natural source clip of the speaker being cloned. The variant letter in the stimulus ID identifies the seed: a Type 3 clone shares its seed clip with the comparison clip of the matched Type 2 pair, and a Type 5 clone shares its seed with the matched Type 4 pair (e.g., the clone in `3_F01B` is seeded from the same F01B source clip used as the comparison in `2_F01B`). - **Voice morphs.** For each of the 100 reference speakers, the latent voice representation of the reference speaker is interpolated toward each of 4 within-group comparison speakers (matched on sociophonetic group, age group, and gender), at 2 distinct recordings per comparison speaker, sampled at 10 morph scales between 0 and 1, plus 1 anchor at scale 1. This yields 4 x 2 x 10 + 1 = 81 Type-6 stimuli per reference speaker (8,100 total). Generated using the voice-morphing feature of the same Cartesia TTS system. - **Listeners.** 1,290 adult English-speaking participants recruited via the Centaur AI platform under an IRB-approved protocol. Consent followed the platform's standard pipeline. Each pair received at least 8 judgments; real-speech pairs (Types 1, 2, 4) received more coverage than synthetic pairs to give tighter consensus estimates on the real-speech reference distribution. ## Considerations for use ### Personally identifying information The dataset names public-figure speakers because the celebrity-stratified design is integral to the benchmark and source recordings are already public. Listener identifiers in `participant_responses.csv` are pseudonymized integers tied to no external account. ### Biases and limitations - English-speaking listener pool, US-dialect speakers. Cross-language perception is not measured. - 100 speakers limits statistical power for some subgroup contrasts (20 speakers per sociophonetic group). - Studio-quality audio. In-the-wild conditions (noise, codec compression, telephony) are not represented. - The operational target is a population consensus, appropriate for ambiguous stimuli where any absolute identity label would itself be probabilistic. ### Responsible use The benchmark measures model-human alignment at the evaluation level. We do not release clone-generation recipes or adversarial training targets. Voice-cloning systems that better align with human perception could inform adversarial use; the same alignment knowledge also strengthens defenses (perception-aligned identity models can flag clones that metadata-based verification accepts). ## License - **Dataset (audio, judgments, metadata, embeddings):** Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0). See `LICENSE`. - **Code (scripts, notebook):** MIT License. See `LICENSE-CODE`. - **Pretrained model weights** (loaded by extraction scripts): each baseline retains its original license; see `docs/model_table.md`. Commercial use of the audio, judgments, or derived embeddings is not permitted under this license. ## Citation To be filled in at camera-ready. ``` @inproceedings{vipbench2026, title = {VIPBench: A Human-Aligned Benchmark for Voice Identity Perception in the Age of Voice Cloning}, author = {Anonymous}, booktitle = {Advances in Neural Information Processing Systems Datasets and Benchmarks}, year = {2026}, note = {Anonymized for double-blind review.} } ``` ## Files - `README.md` (this file): dataset card. - `LICENSE`: CC-BY-NC 4.0 full text. - `LICENSE-CODE`: MIT full text for scripts. - `croissant.json`: MLCommons Croissant 1.0 metadata (core + Responsible AI fields). - `DATASHEET.md`: Datasheet for Datasets (Gebru et al. 2021). - `CHANGELOG.md`: version history. - `CITATION.cff`: machine-readable citation. - `requirements.txt`: pinned Python dependencies. - `data/`, `samples/`, `code/`, `docs/`: see structure section above.