SPARC: Spoken Profile Authorship Recognition Corpus

SPARC is a benchmark for measuring how much speaker identity and demographic attributes (sex, age, accent) leak from transcribed speech. It contains quality-filtered, single-speaker podcast transcripts from two corpora, NaturalVoices and SPoRC, with speaker-level attribute labels.

This repository contains the speaker-level data only. The code, the verification-trial generation used in the paper (identity, sex, age, and accent trials; probe length P ∈ {50, 200} words; enrollment size L ∈ {5, 20} utterances), and the evaluation scripts are on GitHub: link

Files

File	Source	Speakers	Episodes	Utterances	Words	Size
naturalvoices_speakers.json	NaturalVoices (CC-BY 4.0 episodes)	2,344	3,500	779,494	13.7M	87 MB
sporc_speakers.json	SPoRC (single-speaker subset)	10,165	15,555	795,591	26.5M	153 MB

Both files share the same structure.

Structure

Each file is a JSON object keyed by speaker ID. Each speaker maps to one key per episode (that speaker's utterances in that episode) plus five attribute keys:

{
  "n00030": {
    "MSP-PODCAST_0001": [
      "Hey, you are listening to Nothing Sounds Better Podcast.",
      "..."
    ],
    "age": 28.78,
    "gender": "male",
    "accent": "North America",
    "accent_probs": {"Eastern Asia": 0.0001, "English": 0.001, "...": "...", "North America": 0.9688, "Welsh": 0.0},
    "accent_num_utterances": 16
  }
}

Key	Type	Description
<episode_id>	list[str]	The speaker's retained utterances in that episode, in temporal order. Every key that is not one of the five attributes below is an episode.
age	float	Predicted age in years, averaged over utterances.
gender	str	Predicted sex: male or female. NaturalVoices also contains child for 5 speakers.
accent	str	Argmax of accent_probs.
accent_probs	dict[str, float]	Speaker-level distribution over 16 English dialect classes: Eastern Asia, English, Germanic, Irish, North America, Northern Irish, Oceania, Other, Romance, Scottish, Semitic, Slavic, South African, South-eastern Asia, Southern Asia, Welsh.
accent_num_utterances	int	Number of utterance-level predictions averaged into accent_probs.

Identifiers

	NaturalVoices	SPoRC
Speaker ID	n + original NaturalVoices speaker ID, zero-padded (n00030 = speaker 30)	s00000–s10164, pseudonymous; speaker names are not released
Episode ID	MSP-PODCAST_####, from NaturalVoices	ep_######, internal episode index
Episodes	May contain several speakers (1,699 of 3,500)	Single-speaker

Loading

The files are nested JSON (one object per speaker), so load_dataset and the dataset viewer do not apply. Load them directly:

import json
from huggingface_hub import hf_hub_download

ATTRS = {"age", "gender", "accent", "accent_probs", "accent_num_utterances"}

path = hf_hub_download("timtsapras23/SPARC", "naturalvoices_speakers.json", repo_type="dataset")
with open(path) as f:
    data = json.load(f)

for speaker_id, info in data.items():
    episodes = {k: v for k, v in info.items() if k not in ATTRS}  
    labels = {k: info[k] for k in ATTRS}

Use the GitHub scripts to build the verification trials from these files.

Construction

SPARC is built from two English podcast corpora: NaturalVoices (Du et al., 2025) and SPoRC (Litterer et al., 2025). Speaker labels are either taken from the original data or inferred with speech models:

• Age and sex. Taken from the original metadata for NaturalVoices; inferred with Vox-Profile (Feng et al., 2025b) for SPoRC.
• Accent. Inferred with Voxlect (Feng et al., 2025a) for both corpora.

Details are in the paper.

Statistics

	NaturalVoices	SPoRC
Speakers	2,344	10,165
Unique episodes	3,500	15,555
Speaker–episode pairs	6,766	15,555
Utterances	779,494	795,591
Avg. utterances / speaker	332.6	78.3
Mean age (years)	41.5	37.9
Male / Female / Child (%)	52.4 / 47.4 / 0.2	59.3 / 40.7 / 0
Age < 30 / 30–60 / 60+ (%)	15.7 / 76.2 / 8.1	36.3 / 53.1 / 10.6
North American accent (%)	78.2	77.3

Intended Use

SPARC is intended for research on transcript privacy: measuring identity and attribute leakage from transcribed speech, and developing and evaluating text-level anonymization and other defenses.

Out of scope: re-identifying, profiling, or making decisions about individuals, including forensic, law-enforcement, employment, eligibility, or other high-stakes uses.

License

• naturalvoices_speakers.json: derived from NaturalVoices episodes released under CC-BY 4.0; redistributed under CC-BY 4.0. Please credit NaturalVoices (Du et al., 2025).
• sporc_speakers.json: derived from SPoRC; use is subject to the SPoRC data-use terms (research and education only, no commercial use). Please credit SPoRC (Litterer et al., 2025).
• Accent labels are produced with Voxlect; reuse should comply with Voxlect's Responsible AI License.

Please also cite the source corpora and labeling models:

• Du et al. 2025. NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion. arXiv:2511.00256.
• Litterer, Jurgens, and Card. 2025. Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus. ACL 2025.
• Feng et al. 2025. Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits. arXiv:2505.14648.
• Feng et al. 2025. Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and Regional Languages Around the Globe. arXiv:2508.01691.