Datasets:
pretty_name: 'BLEEP: Broadcast Language Elicitation and Evaluation for Profanity'
language:
- en
language_details: en-US, en-GB
multilinguality:
- monolingual
license: other
license_name: bleep-dua
license_link: LICENSE
task_categories:
- audio-classification
task_ids:
- keyword-spotting
annotations_creators:
- other
language_creators:
- crowdsourced
source_datasets:
- original
size_categories:
- 1K<n<10K
tags:
- audio
- keyword-spotting
- profanity
- hard-negatives
- accented-english
extra_gated_heading: Request access to BLEEP
extra_gated_description: >-
Access is granted to individuals, not organisations, and is reviewed manually.
Please allow a few days for a decision.
extra_gated_button_content: Accept the DUA and request access
extra_gated_prompt: >-
BLEEP is released for non-commercial academic research only, under a Data Use
Agreement. You agree not to (a) attempt to re-identify any speaker; (b) use
the recordings for voice cloning, speaker verification, surveillance, or
harassment; (c) redistribute the audio or metadata; (d) use the data
commercially. Commercial licences are separate and administered by the
maintainer. The dataset contains strong profanity and sexually explicit
lexical items.
extra_gated_fields:
Full name: text
Institutional email: text
Affiliation: text
Country: country
Intended research use: text
I have read and agree to the BLEEP Data Use Agreement: checkbox
I will not attempt to re-identify speakers or use the data for voice cloning: checkbox
I will not redistribute the data or use it commercially: checkbox
BLEEP — Broadcast Language Elicitation and Evaluation for Profanity
v2.0 · 8,312 clips · 86 speakers · English (US/UK) · 16 kHz mono · 4.62 h
Isolated-word English corpus for profanity speech research. 20 profanity keywords and 29 hard negatives — minimal-pair confusables selected by CMUdict phoneme edit distance and SUBTLEX-US/UK frequency. Each speaker recorded all 49 words twice in one session, once in a neutral and once in an expressive register.
⚠️ Content warning — every clip is a spoken profanity or a near-homophone of one. 🔒 Gated, research-only, non-commercial under a Data Use Agreement.
Composition
| Clips / speakers | 8,312 / 86 (ID00001–ID00086), mean 96.7 each |
| Vocabulary | 49 words — 20 profanity, 29 hard negatives |
| Registers | neutral 4,169 · expressive 4,143 |
| Labels | 3,384 profanity / 4,928 non_profanity |
| Audio | 16 kHz mono 16-bit WAV, fixed 2.000 s, word onset at 200 ms |
| Countries | US 49 / UK 37 · collected 20–30 June 2026 via Prolific |
Word list
12 base words, 8 morphological variants, 29 hard negatives — 49 in total. Hard negatives are
at phoneme edit distance d = 1 from their base word in every case except mustard (d = 2).
| Base word | Variants | Hard negatives |
|---|---|---|
| fuck | fucking, fucked, fucker | duck, luck, suck, buck |
| shit | bullshit, shitty | sit, shot, shut, ship, sheet |
| cunt | — | count, hunt |
| piss | — | miss, kiss, piece, pick |
| tits | — | bits, hits, sits, tips |
| motherfucker | motherfucking | none |
| cocksucker | — | none |
| bitch | bitches, bitching | beach, pitch, witch, ditch |
| ass | — | gas, pass, mass, lass |
| pussy | — | pushy |
| bastard | — | mustard |
| asshole | — | none |
Base words and variants are label = profanity; hard negatives are label = non_profanity.
All 49 words are recorded in both registers. cocksucker, motherfucker and asshole have no
hard negatives — no CMUdict+SUBTLEX neighbours exist at d ≤ 2.
Not included: silence/background and unknown/filler classes, connected speech, codec- or noise-processed variants, TTS augmentation, phone alignments.
Files
clips/ID000NN/ID000NN_{word}_{register}.wav
metadata.csv # HF loader index (file_name + manifest columns)
manifest.csv # clip_path, speaker_id, word, register, tier, label,
# word_onset_s, word_offset_s, clip_ms
demographics.csv # speaker_id, gender, age_range, background, accent_dialect,
# country, languages, english_acquisition_age, media_exposure
label ∈ {profanity, non_profanity}. word_onset_s and word_offset_s are in the source
recording's timebase. Clips are cut at onset − 200 ms with fixed 2 s length; within a clip the
word spans [200 ms, 200 + (offset − onset)]. Windows extending past the source are zero-padded.
Demographics are self-reported and categorical. Free-text region was dropped and generalised to
country. accent_dialect is pipe-delimited multi-select; an empty value is a non-response.
Speakers
| Field | Distribution (n = 86) |
|---|---|
gender |
Female 45 · Male 39 · Non-binary 2 |
age_range |
36–45: 26 · 26–35: 23 · 46–55: 16 · 18–25: 12 · 56–65: 8 · 65+: 1 |
country |
US 49 · UK 37 |
background |
white 48 · black 20 · hispanic/latino 5 · south asian 5 · MENA 2 · mixed 2 · asian 1 · unstated 3 |
languages |
english_only 74 · english_plus 8 · unstated 4 |
english_acquisition_age |
from birth 72 · before 5: 6 · age 5–10: 2 · age 18+: 1 · unstated 5 |
media_exposure |
mostly US 43 · mostly UK 19 · mixed 17 · unstated 7 |
accent_dialect is multi-select: 116 tags across 86 speakers in 29 distinct combinations. The
column below sums to 116, not 86.
| Accent / dialect | Speakers |
|---|---|
| General American | 26 |
| Southern US | 16 |
| Standard Southern British / RP-like | 12 |
| African American English | 10 |
| New York / Northeast US | 9 |
| Northern England | 9 |
| Western US / California | 6 |
| Midlands English | 6 |
| London Estuary | 5 |
| Midwest US | 4 |
| Scottish English | 4 |
| Hispanic/Latino English | 3 |
| Welsh English | 2 |
| Northern Irish English | 2 |
| Asian American English | 1 |
| Unknown | 1 |
Collection and QC
Recorded through a web app (Chrome desktop) in ~8–10 min: mic check, 49 words neutral, 49 words expressive, demographic questionnaire. Prompt order was randomised per speaker. Each trial captured 2.5 s at 48 kHz — a 500 ms lead-in followed by a 2 s speak window. Participants could skip any word or stop early without affecting payment. The expressive prompt read "as in a TV or film scene, never shouted or directed at anyone".
QC was run independently of alignment: Silero VAD localisation (threshold 0.5), duration, clipping and segmental-SNR checks (flag only), and Whisper large-v3 with a primed initial prompt for substitution detection. A recording was rejected only where VAD found no word and Whisper did not confirm it. Speakers retaining fewer than 60 clips were excluded.
Of 9,286 recordings: 8,312 accepted (89.5%), 865 dropped with an excluded speaker (9 speakers), 109 rejected individually.
Alignment used MFA 3.x with the english_us_arpa acoustic model and a supplementary profanity
dictionary.
Uses
Permitted: profanity keyword-spotting research; phonetic confusability and false-alarm benchmarking; research on ASR profanity suppression.
Prohibited under the DUA: voice cloning, TTS, and speaker verification on any released speaker; speaker re-identification; surveillance and harassment; commercial use; redistribution; training offensive-content generators.
Consent — what participants were shown
Participants read an information sheet and ticked each item separately before recording. Responses were logged with the document version and a timestamp.
Required consent items:
| # | Item |
|---|---|
| 1 | Aged 18+, has read and understood the document |
| 2 | Understands the recordings involve profane language and is willing to produce it; may skip, stop, or withdraw |
| 3 | Explicit consent to biometric processing under UK GDPR Art. 9(2)(a), including transfer to the controller in India and to DUA recipients |
| 4 | Grants a perpetual, worldwide, non-exclusive licence |
| 5 | Understands the dataset may be commercially licensed (excluding synthetic derivatives) with no further compensation |
| 6 | Understands and accepts the re-identification risk |
| 7 | Consents to demographic information being collected, stored alongside the recordings, and disclosed to approved researchers and reviewers |
Licence
Research-only, non-commercial, gated release, per person, under a Data Use Agreement — not CC-BY, and redistribution is prohibited. The maintainer holds a non-exclusive licence from each speaker and separately administers commercial licences covering the original recordings only, excluding synthetic derivatives. Re-releasing openly under CC-BY would require re-consent, and an erasure request can remove a speaker from a released version.
Maintainer: Ritin Raveendran Kasthuri — contact@methodosprojects.org
Usage
from datasets import load_dataset, Audio
import numpy as np
# Gated: requires an approved access request and `hf auth login`.
# The corpus is one undivided partition, named "train" by convention. No splits are shipped.
ds = load_dataset("<org>/BLEEP", split="train").cast_column("audio", Audio(sampling_rate=16_000))
# Speaker-disjoint split.
spk = sorted(set(ds["speaker_id"]))
np.random.default_rng(0).shuffle(spk)
n = len(spk)
test, dev = set(spk[: n // 7]), set(spk[n // 7 : 2 * n // 7])
train = set(spk) - test - dev
splits = {k: ds.filter(lambda r, s=s: r["speaker_id"] in s)
for k, s in [("train", train), ("dev", dev), ("test", test)]}
# Word extent within a clip.
word_span_ms = lambda r: (200.0, 200.0 + (r["word_offset_s"] - r["word_onset_s"]) * 1000)
Citation
@misc{kasthuri2026bleep,
title = {{BLEEP}: Broadcast Language Elicitation and Evaluation for Profanity},
author = {Kasthuri, Ritin Raveendran},
year = {2026},
version = {2.0},
note = {English profanity keyword-spotting corpus with systematic phonetic hard
negatives. Gated research-only release under a Data Use Agreement.},
howpublished = {\url{https://huggingface.co/datasets/<org>/BLEEP}}
}